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1. BCN20000: Dermoscopic Lesions in the Wild

Author

Hernández-Pérez, Carlos, Combalia, Marc, Podlipnik, Sebastian, Codella, Noel C. F., Rotemberg, Veronica, Halpern, Allan C., Reiter, Ofer, Carrera, Cristina, Barreiro, Alicia, Helba, Brian, Puig, Susana, Vilaplana, Veronica, and Malvehy, Josep

Published
2024
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2. Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy

Author

Liopyris, Konstantinos, Navarrete-Dechent, Cristian, Marchetti, Michael A., Rotemberg, Veronica, Apalla, Zoe, Argenziano, Giuseppe, Blum, Andreas, Braun, Ralph P., Carrera, Cristina, Codella, Noel C.F., Combalia, Marc, Dusza, Stephen W., Gutman, David A., Helba, Brian, Hofmann-Wellenhof, Rainer, Jaimes, Natalia, Kittler, Harald, Kose, Kivanc, Lallas, Aimilios, Longo, Caterina, Malvehy, Josep, Menzies, Scott, Nelson, Kelly C., Paoli, John, Puig, Susana, Rabinovitz, Harold S., Rishpon, Ayelet, Russo, Teresa, Scope, Alon, Soyer, H. Peter, Stein, Jennifer A., Stolz, Willhelm, Sgouros, Dimitrios, Stratigos, Alexander J., Swanson, David L., Thomas, Luc, Tschandl, Philipp, Zalaudek, Iris, Weber, Jochen, Halpern, Allan C., and Marghoob, Ashfaq A.

Published
2024
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3. BCN20000: Dermoscopic Lesions in the Wild

Author

Combalia, Marc, Codella, Noel C. F., Rotemberg, Veronica, Helba, Brian, Vilaplana, Veronica, Reiter, Ofer, Carrera, Cristina, Barreiro, Alicia, Halpern, Allan C., Puig, Susana, and Malvehy, Josep

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl\'inic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019, where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically., Comment: Abstract for BCN20000

Published
2019

4. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Author

Codella, Noel, Rotemberg, Veronica, Tschandl, Philipp, Celebi, M. Emre, Dusza, Stephen, Gutman, David, Helba, Brian, Kalloo, Aadi, Liopyris, Konstantinos, Marchetti, Michael, Kittler, Harald, and Halpern, Allan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin. The challenge was hosted in 2018 at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Granada, Spain. The dataset included over 12,500 images across 3 tasks. 900 users registered for data download, 115 submitted to the lesion segmentation task, 25 submitted to the lesion attribute detection task, and 159 submitted to the disease classification task. Novel evaluation protocols were established, including a new test for segmentation algorithm performance, and a test for algorithm ability to generalize. Results show that top segmentation algorithms still fail on over 10% of images on average, and algorithms with equal performance on test data can have different abilities to generalize. This is an important consideration for agencies regulating the growing set of machine learning tools in the healthcare domain, and sets a new standard for future public challenges in healthcare., Comment: https://challenge2018.isic-archive.com/

Published
2019

5. Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)

Author

Codella, Noel C. F., Gutman, David, Celebi, M. Emre, Helba, Brian, Marchetti, Michael A., Dusza, Stephen W., Kalloo, Aadi, Liopyris, Konstantinos, Mishra, Nabin, Kittler, Harald, and Halpern, Allan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.

Published
2017

6. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Author

Combalia, Marc, Codella, Noel, Rotemberg, Veronica, Carrera, Cristina, Dusza, Stephen, Gutman, David, Helba, Brian, Kittler, Harald, Kurtansky, Nicholas R, Liopyris, Konstantinos, Marchetti, Michael A, Podlipnik, Sebastian, Puig, Susana, Rinner, Christoph, Tschandl, Philipp, Weber, Jochen, Halpern, Allan, and Malvehy, Josep

Published
2022
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7. Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images

Author

Codella, Noel, Nguyen, Quoc-Bao, Pankanti, Sharath, Gutman, David, Helba, Brian, Halpern, Allan, and Smith, John R.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%)., Comment: URL for the IBM Journal of Research and Development: http://www.research.ibm.com/journal/

Published
2016

8. Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

Author

Gutman, David, Codella, Noel C. F., Celebi, Emre, Helba, Brian, Marchetti, Michael, Mishra, Nabin, and Halpern, Allan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.

Published
2016

10. BCN20000: dermoscopic lesions in the wild

Author

Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Hernández Pérez, Carlos, Combalia Escudero, Marc, Podlipnik, Sebastian, Codella, Noel C. F., Rotemberg, Veronica, Halpern, Allan C., Reiter, Ofer, Carrera Álvarez, Cristina, Barreiro Capurro, Alicia, Helba, Brian, Puig Sardá, Susana, Vilaplana Besler, Verónica, Malvehy Guilera, Josep, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Hernández Pérez, Carlos, Combalia Escudero, Marc, Podlipnik, Sebastian, Codella, Noel C. F., Rotemberg, Veronica, Halpern, Allan C., Reiter, Ofer, Carrera Álvarez, Cristina, Barreiro Capurro, Alicia, Helba, Brian, Puig Sardá, Susana, Vilaplana Besler, Verónica, and Malvehy Guilera, Josep

Abstract

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation., We acknowledge the support of the International Skin Imaging Collaboration (ISIC). This research was supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the project 718/C/2019 with id 201923-30 and 201923-31, funded by Fundació la Marató de TV3, iTOBOs grant from the European Union’s Horizon 2020 research and innovation programme num 965221. Other funding sources include the Melanoma Research Alliance Young Investigator Award 614197. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748., Peer Reviewed, Postprint (published version)

Published
2024

11. A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Author

Rotemberg, Veronica, Kurtansky, Nicholas, Betz-Stablein, Brigid, Caffery, Liam, Chousakos, Emmanouil, Codella, Noel, Combalia, Marc, Dusza, Stephen, Guitera, Pascale, Gutman, David, Halpern, Allan, Helba, Brian, Kittler, Harald, Kose, Kivanc, Langer, Steve, Lioprys, Konstantinos, Malvehy, Josep, Musthaq, Shenara, Nanda, Jabpani, Reiter, Ofer, Shih, George, Stratigos, Alexander, Tschandl, Philipp, Weber, Jochen, and Soyer, H. Peter

Published
2021
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12. Publisher Correction: Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Author

Rotemberg, Veronica, Kurtansky, Nicholas, Betz-Stablein, Brigid, Caffery, Liam, Chousakos, Emmanouil, Codella, Noel, Combalia, Marc, Dusza, Stephen, Guitera, Pascale, Gutman, David, Halpern, Allan, Helba, Brian, Kittler, Harald, Kose, Kivanc, Langer, Steve, Lioprys, Konstantinos, Malvehy, Josep, Musthaq, Shenara, Nanda, Jabpani, Reiter, Ofer, Shih, George, Stratigos, Alexander, Tschandl, Philipp, Weber, Jochen, and Soyer, H. Peter

Published
2021
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13. Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Author

Rotemberg, Veronica, Kurtansky, Nicholas, Betz-Stablein, Brigid, Caffery, Liam, Chousakos, Emmanouil, Codella, Noel, Combalia, Marc, Dusza, Stephen, Guitera, Pascale, Gutman, David, Halpern, Allan, Helba, Brian, Kittler, Harald, Kose, Kivanc, Langer, Steve, Lioprys, Konstantinos, Malvehy, Josep, Musthaq, Shenara, Nanda, Jabpani, Reiter, Ofer, Shih, George, Stratigos, Alexander, Tschandl, Philipp, Weber, Jochen, and Soyer, H. Peter

Published
2021
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14. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

Author

Tschandl, Philipp, Codella, Noel, Akay, Bengü Nisa, Argenziano, Giuseppe, Braun, Ralph P, Cabo, Horacio, Gutman, David, Halpern, Allan, Helba, Brian, Hofmann-Wellenhof, Rainer, Lallas, Aimilios, Lapins, Jan, Longo, Caterina, Malvehy, Josep, Marchetti, Michael A, Marghoob, Ashfaq, Menzies, Scott, Oakley, Amanda, Paoli, John, Puig, Susana, Rinner, Christoph, Rosendahl, Cliff, Scope, Alon, Sinz, Christoph, Soyer, H Peter, Thomas, Luc, Zalaudek, Iris, and Kittler, Harald

Published
2019
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15. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Author

Marchetti, Michael A., Codella, Noel C.F., Dusza, Stephen W., Gutman, David A., Helba, Brian, Kalloo, Aadi, Mishra, Nabin, Carrera, Cristina, Celebi, M. Emre, DeFazio, Jennifer L., Jaimes, Natalia, Marghoob, Ashfaq A., Quigley, Elizabeth, Scope, Alon, Yélamos, Oriol, and Halpern, Allan C.

Published
2018
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16. Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus

Author

Adhikari, Bandita, primary, Scindia, Yogesh, additional, Sordo Vieira, Luis, additional, de Assis Lopes Ribeiro, Henrique, additional, Masison, Joseph, additional, Yang, Ning, additional, Fonseca, Luis L., additional, Wheeler, Matthew, additional, Knapp, Adam C., additional, Mei, Yu, additional, Helba, Brian, additional, Atkinson, Carl, additional, Schroeder, Will, additional, Mehrad, Borna, additional, and Laubenbacher, Reinhard, additional

Published
2022
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17. Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group

Author

Daneshjou, Roxana Barata, Catarina Betz-Stablein, Brigid and Celebi, M. Emre Codella, Noel Combalia, Marc Guitera, Pascale Gutman, David Halpern, Allan Helba, Brian and Kittler, Harald Kose, Kivanc Liopyris, Konstantinos Malvehy, Josep Seog, Han Seung Soyer, H. Peter Tkaczyk, Eric R. and Tschandl, Philipp Rotemberg, Veronica and Daneshjou, Roxana Barata, Catarina Betz-Stablein, Brigid and Celebi, M. Emre Codella, Noel Combalia, Marc Guitera, Pascale Gutman, David Halpern, Allan Helba, Brian and Kittler, Harald Kose, Kivanc Liopyris, Konstantinos Malvehy, Josep Seog, Han Seung Soyer, H. Peter Tkaczyk, Eric R. and Tschandl, Philipp Rotemberg, Veronica

Abstract

IMPORTANCE The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for “artificial intelligence” and “reporting guidelines,” as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus. FINDINGS A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.

Published
2022

18. CheckList for Evaluation of image-based AI Reports in Dermatology: CLEAR Derm Consensus Guidelines from the International Skin Imaging Collaboration Artificial Intelligence Working Group

Author

Daneshjou, Roxana, Barata, Catarina, Betz-Stablein, Brigid, Celebi, M. Emre, Codella, Noel, Combalia, Marc, Guitera, Pascale, Gutman, David, Halpern, Allan, Helba, Brian, Kittler, Harald, Kose, Kivanc, Liopyris, Konstantinos, Malvehy, Josep, Seog, Han Seung, Soyer, H. Peter, Tkaczyk, Eric R, Tschandl, Philipp, and Rotemberg, Veronica

Subjects
Article
Abstract

IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW: In this consensus statement, the 19 members of the International Skin Imaging Collaboration (ISIC) AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008 and August 24, 2021 for “Artificial Intelligence” and “Reporting Guidelines,” as well as other pertinent studies identified by the expert panel. Factors that are viewed as critical to AI development and performance evaluation were included and underwent two rounds of electronic discussion to achieve consensus. FINDINGS: We define a checklist of items that outline best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.

Published
2022

19. Computational modeling of macrophage iron sequestration during host defense againstAspergillus

Author

Adhikari, Bandita, primary, Scindia, Yogesh, additional, Vieira, Luis Sordo, additional, de Assis Lopes Ribeiro, Henrique, additional, Masison, Joseph, additional, Yang, Ning, additional, Fonseca, Luis L., additional, Wheeler, Matthew, additional, Knapp, Adam C., additional, Mei, Yu, additional, Helba, Brian, additional, Atkinson, Carl, additional, Schroeder, Will, additional, Mehrad, Borna, additional, and Laubenbacher, Reinhard, additional

Published
2022
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20. Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

Author

Daneshjou, Roxana, primary, Barata, Catarina, additional, Betz-Stablein, Brigid, additional, Celebi, M. Emre, additional, Codella, Noel, additional, Combalia, Marc, additional, Guitera, Pascale, additional, Gutman, David, additional, Halpern, Allan, additional, Helba, Brian, additional, Kittler, Harald, additional, Kose, Kivanc, additional, Liopyris, Konstantinos, additional, Malvehy, Josep, additional, Seog, Han Seung, additional, Soyer, H. Peter, additional, Tkaczyk, Eric R., additional, Tschandl, Philipp, additional, and Rotemberg, Veronica, additional

Published
2022
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21. A multimodal cell census and atlas of the mammalian primary motor cortex

Author

Callaway, Edward M., Dong, Hong-Wei, Ecker, Joseph R., Hawrylycz, Michael J., Huang, Z. Josh, Lein, Ed S., Ngai, John, Osten, Pavel, Ren, Bing, Tolias, Andreas Savas, White, Owen, Zeng, Hongkui, Zhuang, Xiaowei, Ascoli, Giorgio A., Behrens, M. Margarita, Chun, Jerold, Feng, Guoping, Gee, James C., Ghosh, Satrajit S., Halchenko, Yaroslav O., Hertzano, Ronna, Lim, Byung Kook, Martone, Maryann E., Ng, Lydia, Pachter, Lior, Ropelewski, Alexander J., Tickle, Timothy L., Yang, X. William, Zhang, Kun, Bakken, Trygve E., Berens, Philipp, Daigle, Tanya L., Harris, Julie A., Jorstad, Nikolas L., Kalmbach, Brian E., Kobak, Dmitry, Li, Yang Eric, Liu, Hanqing, Matho, Katherine S., Mukamel, Eran A., Naeemi, Maitham, Scala, Federico, Tan, Pengcheng, Ting, Jonathan T., Xie, Fangming, Zhang, Meng, Zhang, Zhuzhu, Zhou, Jingtian, Zingg, Brian, Armand, Ethan, Yao, Zizhen, Bertagnolli, Darren, Casper, Tamara, Crichton, Kirsten, Dee, Nick, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Dougherty, Elizabeth L., Fong, Olivia, Goldman, Melissa, Goldy, Jeff, Hodge, Rebecca D., Hu, Lijuan, Keene, C. Dirk, Krienen, Fenna M., Kroll, Matthew, Lake, Blue B., Lathia, Kanan, Linnarsson, Sten, Liu, Christine S., Macosko, Evan Z., McCarroll, Steven A., McMillen, Delissa, Nadaf, Naeem M., Nguyen, Thuc Nghi, Palmer, Carter R., Pham, Thanh, Plongthongkum, Nongluk, Reed, Nora M., Regev, Aviv, Rimorin, Christine, Romanow, William J., Savoia, Steven, Siletti, Kimberly, Smith, Kimberly, Sulc, Josef, Tasic, Bosiljka, Tieu, Michael, Torkelson, Amy, Tung, Herman, van Velthoven, Cindy T. J., Vanderburg, Charles R., Yanny, Anna Marie, Fang, Rongxin, Hou, Xiaomeng, Lucero, Jacinta D., Osteen, Julia K., Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Wang, Xinxin, Aldridge, Andrew I., Bartlett, Anna, Boggeman, Lara, O’Connor, Carolyn, Castanon, Rosa G., Chen, Huaming, Fitzpatrick, Conor, Luo, Chongyuan, Nery, Joseph R., Nunn, Michael, Rivkin, Angeline C., Tian, Wei, Dominguez, Bertha, Ito-Cole, Tony, Jacobs, Matthew, Jin, Xin, Lee, Cheng-Ta, Lee, Kuo-Fen, Miyazaki, Paula Assakura, Pang, Yan, Rashid, Mohammad, Smith, Jared B., Vu, Minh, Williams, Elora, Biancalani, Tommaso, Booeshaghi, A. Sina, Crow, Megan, Dudoit, Sandrine, Fischer, Stephan, Gillis, Jesse, Hu, Qiwen, Kharchenko, Peter V., Niu, Sheng-Yong, Ntranos, Vasilis, Purdom, Elizabeth, Risso, Davide, de Bézieux, Hector Roux, Somasundaram, Saroja, Street, Kelly, Svensson, Valentine, Vaishnav, Eeshit Dhaval, Van den Berge, Koen, Welch, Joshua D., An, Xu, Bateup, Helen S., Bowman, Ian, Chance, Rebecca K., Foster, Nicholas N., Galbavy, William, Gong, Hui, Gou, Lin, Hatfield, Joshua T., Hintiryan, Houri, Hirokawa, Karla E., Kim, Gukhan, Kramer, Daniel J., Li, Anan, Li, Xiangning, Luo, Qingming, Muñoz-Castañeda, Rodrigo, Stafford, David A., Feng, Zhao, Jia, Xueyan, Jiang, Shengdian, Jiang, Tao, Kuang, Xiuli, Larsen, Rachael, Lesnar, Phil, Li, Yaoyao, Li, Yuanyuan, Liu, Lijuan, Peng, Hanchuan, Qu, Lei, Ren, Miao, Ruan, Zongcai, Shen, Elise, Song, Yuanyuan, Wakeman, Wayne, Wang, Peng, Wang, Yimin, Wang, Yun, Yin, Lulu, Yuan, Jing, Zhao, Sujun, Zhao, Xuan, Narasimhan, Arun, Palaniswamy, Ramesh, Banerjee, Samik, Ding, Liya, Huilgol, Dhananjay, Huo, Bingxing, Kuo, Hsien-Chi, Laturnus, Sophie, Li, Xu, Mitra, Partha P., Mizrachi, Judith, Wang, Quanxin, Xie, Peng, Xiong, Feng, Yu, Yang, Eichhorn, Stephen W., Berg, Jim, Bernabucci, Matteo, Bernaerts, Yves, Cadwell, Cathryn René, Castro, Jesus Ramon, Dalley, Rachel, Hartmanis, Leonard, Horwitz, Gregory D., Jiang, Xiaolong, Ko, Andrew L., Miranda, Elanine, Mulherkar, Shalaka, Nicovich, Philip R., Owen, Scott F., Sandberg, Rickard, Sorensen, Staci A., Tan, Zheng Huan, Allen, Shona, Hockemeyer, Dirk, Lee, Angus Y., Veldman, Matthew B., Adkins, Ricky S., Ament, Seth A., Bravo, Héctor Corrada, Carter, Robert, Chatterjee, Apaala, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Felix, Victor, Giglio, Michelle, Herb, Brian R., Kancherla, Jayaram, Mahurkar, Anup, McCracken, Carrie, Nickel, Lance, Olley, Dustin, Orvis, Joshua, Schor, Michael, Hood, Greg, Dichter, Benjamin, Grauer, Michael, Helba, Brian, Bandrowski, Anita, Barkas, Nikolaos, Carlin, Benjamin, D’Orazi, Florence D., Degatano, Kylee, Gillespie, Thomas H., Khajouei, Farzaneh, Konwar, Kishori, Thompson, Carol, Kelly, Kathleen, Mok, Stephanie, Sunkin, Susan, BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, [missing], BICCN contributing principal investigators, [missing], Principal manuscript editors, [missing], Manuscript writing and figure generation, [missing], Analysis coordination, [missing], Integrated data analysis, [missing], scRNA-seq and snRNA-seq data generation and processing, [missing], ATAC-seq data generation and processing, [missing], Methylcytosine data production and analysis, [missing], Epi-retro-seq data generation and processing, [missing], ‘Omics data analysis, [missing], Tracing and connectivity data generation, [missing], Morphology data generation and reconstruction, [missing], OLST/STPT and other data generation, [missing], Morphology, connectivity and imaging analysis, [missing], Spatially resolved single-cell transcriptomics (MERFISH), [missing], Multimodal profiling (Patch-seq), [missing], Transgenic tools, [missing], NeMO archive and analytics, [missing], Brain Image Library (BIL) archive, [missing], DANDI archive, [missing], Brain Cell Data Center (BCDC), [missing], Project management, [missing], and BRAIN Initiative Cell Census Network (BICCN), [missing]

Subjects
Epigenomics, Male, INTERNEURONS, ORGANIZATION, MOUSE, Molecular neuroscience, Neural circuits, CLASSIFICATION, Article, Mice, Atlases as Topic, Glutamates, Species Specificity, Animals, Humans, TRANSCRIPTOMICS, SPECIFICATION, NEURONS, In Situ Hybridization, Fluorescence, Phylogeny, Neurons, Multidisciplinary, Gene Expression Profiling, Biology and Life Sciences, PATHWAYS, Brain, Callithrix, Censuses, Cellular neuroscience, Mathematics and Statistics, SINGLE, Organ Specificity, MORPHOLOGY, Motor cortex, Female, Single-Cell Analysis, Transcriptome
Abstract

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties., The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

Published
2021

22. A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Author

Rotemberg, Veronica Kurtansky, Nicholas Betz-Stablein, Brigid and Caffery, Liam Chousakos, Emmanouil Codella, Noel and Combalia, Marc Dusza, Stephen Guitera, Pascale Gutman, David and Halpern, Allan Helba, Brian Kittler, Harald Kose, Kivanc and Langer, Steve Lioprys, Konstantinos Malvehy, Josep and Musthaq, Shenara Nanda, Jabpani Reiter, Ofer Shih, George and Stratigos, Alexander Tschandl, Philipp Weber, Jochen and Soyer, H. Peter

Abstract

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Published
2021

23. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

Author

Marchetti, Michael A., primary, Liopyris, Konstantinos, additional, Dusza, Stephen W., additional, Codella, Noel C.F., additional, Gutman, David A., additional, Helba, Brian, additional, Kalloo, Aadi, additional, Halpern, Allan C., additional, Soyer, H. Peter, additional, Curiel-Lewandrowski, Clara, additional, Caffery, Liam, additional, and Malvehy, Josep, additional

Published
2020
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24. Results of the 2016 International Skin Imaging Collaboration ISBI Challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Author

Marchetti, Michael A., Codella, Noel C.F., Dusza, Stephen W., Gutman, David A., Helba, Brian, Kalloo, Aadi, Mishra, Nabin, Carrera, Cristina, Celebi, M. Emre, DeFazio, Jennifer L., Jaimes, Natalia, Marghoob, Ashfaq A., Quigley, Elizabeth, Scope, Alon, Yélamos, Oriol, and Halpern, Allan C.

Subjects
Lentigo, Skin Neoplasms, Dermoscopy, Congresses as Topic, Article, Machine Learning, Cross-Sectional Studies, ROC Curve, Humans, Diagnosis, Computer-Assisted, Melanoma, Nevus, Algorithms, Dermatologists
Abstract

Computer vision may aid in melanoma detection.We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant.The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001).The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice.Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.

Published
2017

25. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Author

Codella, Noel C. F., primary, Gutman, David, additional, Celebi, M. Emre, additional, Helba, Brian, additional, Marchetti, Michael A., additional, Dusza, Stephen W., additional, Kalloo, Aadi, additional, Liopyris, Konstantinos, additional, Mishra, Nabin, additional, Kittler, Harald, additional, and Halpern, Allan, additional

Published
2018
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31. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

Author

Allan C. Halpern, Iris Zalaudek, Michael A. Marchetti, Ashfaq A. Marghoob, Christoph Sinz, Caterina Longo, Josep Malvehy, Amanda Oakley, Rainer Hofmann-Wellenhof, Brian Helba, Luc Thomas, Harald Kittler, John Paoli, H. Peter Soyer, Cliff Rosendahl, Alon Scope, Aimilios Lallas, Horacio Cabo, Philipp Tschandl, Christoph Rinner, Giuseppe Argenziano, Jan Lapins, Scott W. Menzies, David A. Gutman, Ralph P. Braun, Noel C. F. Codella, Susana Puig, Bengü Nisa Akay, Tschandl, Philipp, Codella, Noel, Akay, Bengü Nisa, Argenziano, Giuseppe, Braun, Ralph P, Cabo, Horacio, Gutman, David, Halpern, Allan, Helba, Brian, Hofmann-Wellenhof, Rainer, Lallas, Aimilio, Lapins, Jan, Longo, Caterina, Malvehy, Josep, Marchetti, Michael A, Marghoob, Ashfaq, Menzies, Scott, Oakley, Amanda, Paoli, John, Puig, Susana, Rinner, Christoph, Rosendahl, Cliff, Scope, Alon, Sinz, Christoph, Soyer, H Peter, Thomas, Luc, Zalaudek, Iri, Kittler, Harald, Tschandl, P, Codella, N, Akay, Bn, Argenziano, G, Braun, Rp, Cabo, H, Gutman, D, Halpern, A, Helba, B, Hofmann-Wellenhof, R, Lallas, A, Lapins, J, Longo, C, Malvehy, J, Marchetti, Ma, Marghoob, A, Menzies, S, Oakley, A, Paoli, J, Puig, S, Rinner, C, Rosendahl, C, Scope, A, Sinz, C, Soyer, Hp, Thomas, L, Zalaudek, I, and Kittler, H.

Subjects
Seborrheic keratosis, Solar Lentigo, Adult, Male, Skin Neoplasms, Keratosis, MEDLINE, skin lesions, Dermoscopy, Article, Machine Learning, 030207 dermatology & venereal diseases, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Basal cell carcinoma, PIGMENTED SKIN LESION, Medical diagnosis, Skin, Retrospective Studies, Internet, business.industry, Reproducibility of Results, Melanocytic nevus, medicine.disease, Oncology, 030220 oncology & carcinogenesis, Female, business, Algorithm, Pigmentation Disorders, Algorithms
Abstract

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen’s disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p

Published
2019

32. Multi-scale classification based lesion segmentation for dermoscopic images.

Author

Abedini M, Codella N, Chakravorty R, Garnavi R, Gutman D, Helba B, and Smith JR

Subjects
Algorithms, Databases, Factual, Dermoscopy methods, Humans, Machine Learning, Nevus diagnostic imaging, Nevus pathology, Skin pathology, Skin Neoplasms pathology, Image Processing, Computer-Assisted methods, Skin diagnostic imaging, Skin Neoplasms diagnostic imaging
Abstract

This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.

Published
2016
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