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1. A Foundational Multimodal Vision Language AI Assistant for Human Pathology

Author

Lu, Ming Y., Chen, Bowen, Williamson, Drew F. K., Chen, Richard J., Ikamura, Kenji, Gerber, Georg, Liang, Ivy, Le, Long Phi, Ding, Tong, Parwani, Anil V, and Mahmood, Faisal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology using an in-house developed foundational vision encoder pretrained on 100 million histology images from over 100,000 patient cases and 1.18 million pathology image-caption pairs. The vision encoder is then combined with a pretrained large language model and the whole system is finetuned on over 250,000 diverse disease agnostic visual language instructions. We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4. When relevant clinical context is provided with the histology image, PathChat achieved a diagnostic accuracy of 87% on multiple-choice questions based on publicly available cases of diverse tissue origins and disease models. Additionally, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision language AI assistant that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.

Published
2023

2. Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Author

Song, Andrew H., Williams, Mane, Williamson, Drew F. K., Jaume, Guillaume, Zhang, Andrew, Chen, Bowen, Serafin, Robert, Liu, Jonathan T. C., Baras, Alex, Parwani, Anil V., and Mahmood, Faisal

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

Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selecting a few two-dimensional (2D) sections for microscopic evaluation, risking sampling bias and misdiagnosis. Diverse methods for capturing 3D tissue morphologies have been developed, but they have yet had little translation to clinical practice; manual and computational evaluations of such large 3D data have so far been impractical and/or unable to provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy or microcomputed tomography and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting the value of capturing larger extents of heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

Published
2023

3. Towards a Visual-Language Foundation Model for Computational Pathology

Author

Lu, Ming Y., Chen, Bowen, Williamson, Drew F. K., Chen, Richard J., Liang, Ivy, Ding, Tong, Jaume, Guillaume, Odintsov, Igor, Zhang, Andrew, Le, Long Phi, Gerber, Georg, Parwani, Anil V, and Mahmood, Faisal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain and the model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Published
2023

7. List of contributors

Author

Ahn, Joseph C., primary, Anand, Shankara, additional, Arnaout, O., additional, Bandyopadhyay, Anuja, additional, Bartholomew, Erin, additional, Bates, David W., additional, Bender, Sarah M.L., additional, Bhullar, Puneet K., additional, Bhutani, Tina, additional, Boaro, A., additional, Boyer, Edward W., additional, Brown, Ethan D.L., additional, Carreiro, Stephanie, additional, Chahla, Jorge, additional, Chandran, Viji Pulikkel, additional, Cheng, Ching-Yu, additional, Cheung, Carol Y., additional, Choudhary, Anirudh, additional, Choudhury, Avishek, additional, Chung, Mimi, additional, Ciurtin, Coziana, additional, Comfere, Nneka I., additional, Corriveau-Lecavalier, Nick, additional, Côté, Mélina, additional, del Alamo, Diego, additional, Dias, Roger D., additional, Dönnes, Pierre, additional, Duong, Dat, additional, Ebnali, Mahdi, additional, El Sherbini, Adham, additional, ElZarrad, M. Khair, additional, Fakhouri, Tala H., additional, Fakhoury, Marc, additional, Fein, Joshua A., additional, Fischer, Uwe M., additional, Glicksberg, Benjamin S., additional, Goldstein, Cathy, additional, Gonem, Sherif, additional, Green, Darren V.S., additional, Gupta, Raghav, additional, Hakimi, Marwa, additional, Halamka, John D., additional, Han, Christina S., additional, Hannah-Shmouni, Fady, additional, Harrer, Stefan, additional, Hazime, Ali Amer, additional, Howard, Michael A., additional, Hui, Herbert Y.H., additional, Ildardashty, Alexander, additional, J.F. Shaikh, Hashim, additional, Jeliazkov, Jeliazko R., additional, Jones, David T., additional, Juhn, Young J., additional, Jury, Elizabeth C., additional, Kandaswamy, Swaminathan, additional, Kang, Yanna, additional, Kann, Benjamin H., additional, Kaplin, Scott, additional, Karpiak, Joel, additional, Kassir, Elias, additional, Kaur, Harsimran, additional, Khan, Sohil, additional, Khatib, Reem, additional, Kherabi, Yousra, additional, Knake, Lindsey A., additional, Krittanawong, Chayakrit, additional, Kuhn, Veronica C., additional, Kunze, Kyle, additional, Lamarche, Benoît, additional, Laplante, Simon, additional, Lehmann, Lisa Soleymani, additional, Li, Dawei, additional, Liao, Wilson, additional, Likitlersuang, Jirapat, additional, Liu, Qi, additional, Madani, Amin, additional, Malik, Momin M., additional, Mann, Matthias, additional, Masood, Sameer, additional, Mathur, Piyush, additional, McManus, Sean, additional, Menard, Jeffrey, additional, Mivalt, Filip, additional, Murphree, Dennis, additional, Myers, Thomas G., additional, Narayan, Sanjiv M., additional, Natarajan, Vivek, additional, Pandav, Krunal, additional, Parwani, Anil V., additional, Pedraza Bermeo, Adriana Marcela, additional, Peiffer-Smadja, Nathan, additional, Peng, Junjie, additional, Peng, Lily, additional, Peters, Margot S., additional, Petrick, Nicholas, additional, Polce, Evan, additional, Poojari, Pooja Gopal, additional, Price, W. Nicholson, additional, Raghavan, Lavanya, additional, Rajan, Asha K., additional, Ran, An Ran, additional, Rangu, Sowmith, additional, Rashid, Muhammed, additional, Reddy, Charitha D., additional, Rider, Nicholas L., additional, Rigatti, Marc, additional, Rivers, Michael, additional, Robinson, George, additional, Rogers, Albert J., additional, Rogerson, Colin M., additional, Ryu, Euijung, additional, Sahiner, Berkman, additional, Sarnaik, Kunaal, additional, Shafi, Saba, additional, Shah, Vijay H., additional, Shapovalov, Maxim V., additional, Sharma, Samin K, additional, Shekhar, Skand, additional, Shin, Harold, additional, Shouval, Roni, additional, Smith, Kenneth, additional, Sokumbi, Olayemi, additional, Solomon, Benjamin D., additional, Somani, Sulaiman S., additional, Sternke, Matt C., additional, Strauss, Maximillian T., additional, Syrowatka, Ania, additional, Tang, W. H. Wilson, additional, Teven, Chad M., additional, Tewari, Ashutosh Kumar, additional, Thunga, Girish, additional, Urena, Estefania, additional, Verma, Ashish, additional, Vietas, Jay, additional, Waikel, Rebekah L., additional, Wi, Chung-Il, additional, Winter, Meredith C., additional, Wong, Melissa S., additional, Wong, Tien Yin, additional, Wu, Chao-Ping, additional, Yeroushalmi, Samuel, additional, Zenati, Marco A., additional, Zeng, Wen-Feng, additional, and Zheng, Yingfeng, additional

Published
2024
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8. Single-cell Atlas of Penile Cancer Reveals TP53 Mutations as a Driver of an Aggressive Phenotype, Irrespective of Human Papillomavirus Status, and Provides Clues for Treatment Personalization

Author

Elst, Laura, Philips, Gino, Vandermaesen, Kaat, Bassez, Ayse, Lodi, Francesca, Vreeburg, Manon T.A., Brouwer, Oscar R., Schepers, Rogier, Van Brussel, Thomas, Mohanty, Sambit K., Parwani, Anil V., Spans, Lien, Vanden Bempt, Isabelle, Jacomen, Gerd, Baldewijns, Marcella, Lambrechts, Diether, and Albersen, Maarten

Published
2024
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10. Applications of Artificial Intelligence in Breast Pathology

Author

Liu, Yueping, Han, Dandan, Parwani, Anil V., and Li, Zaibo

Subjects
Artificial intelligence -- Technology application, Metastasis -- Technology application, Algorithms -- Technology application, Algorithm, Artificial intelligence, Technology application, Health
Abstract

* Context.--Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semi-quantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology. Objective.--To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes. Data Sources.--We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience. Conclusions.--With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response., Pathology is the gold standard for disease diagnosis. Pathologists are committed to assisting clinicians in the precise treatment of various diseases, especially malignant tumors. The advance in individualized treatment requires [...]

Published
2023
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11. Analysis of 3D pathology samples using weakly supervised AI

Author

Song, Andrew H., Williams, Mane, Williamson, Drew F.K., Chow, Sarah S.L., Jaume, Guillaume, Gao, Gan, Zhang, Andrew, Chen, Bowen, Baras, Alexander S., Serafin, Robert, Colling, Richard, Downes, Michelle R., Farré, Xavier, Humphrey, Peter, Verrill, Clare, True, Lawrence D., Parwani, Anil V., Liu, Jonathan T.C., and Mahmood, Faisal

Published
2024
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15. Digital cytology part 1: digital cytology implementation for practice: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Author

Kim, David, Sundling, Kaitlin E., Virk, Renu, Thrall, Michael J., Alperstein, Susan, Bui, Marilyn M., Chen-Yost, Heather, Donnelly, Amber D., Lin, Oscar, Liu, Xiaoying, Madrigal, Emilio, Michelow, Pamela, Schmitt, Fernando C., Vielh, Philippe R., Zakowski, Maureen F., Parwani, Anil V., Jenkins, Elizabeth, Siddiqui, Momin T., Pantanowitz, Liron, and Li, Zaibo

Published
2024
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16. Digital cytology part 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Author

Kim, David, Sundling, Kaitlin E., Virk, Renu, Thrall, Michael J., Alperstein, Susan, Bui, Marilyn M., Chen-Yost, Heather, Donnelly, Amber D., Lin, Oscar, Liu, Xiaoying, Madrigal, Emilio, Michelow, Pamela, Schmitt, Fernando C., Vielh, Philippe R., Zakowski, Maureen F., Parwani, Anil V., Jenkins, Elizabeth, Siddiqui, Momin T., Pantanowitz, Liron, and Li, Zaibo

Published
2024
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18. The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force

Author

Kim, David, Thrall, Michael J., Michelow, Pamela, Schmitt, Fernando C., Vielh, Philippe R., Siddiqui, Momin T., Sundling, Kaitlin E., Virk, Renu, Alperstein, Susan, Bui, Marilyn M., Chen-Yost, Heather, Donnelly, Amber D., Lin, Oscar, Liu, Xiaoying, Madrigal, Emilio, Zakowski, Maureen F., Parwani, Anil V., Jenkins, Elizabeth, Pantanowitz, Liron, and Li, Zaibo

Published
2024
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36. Contributors

Author

Brimo, Fadi, primary, Chaux, Alcides, additional, Chen, Ying-Bei, additional, Baydar, Dilek Ertoy, additional, Genega, Elizabeth M., additional, Gordetsky, Jennifer, additional, Guo, Charles C., additional, Hirsch, Michelle S., additional, Kryvenko, Oleksandr N., additional, Latour, Mathieu, additional, Lotan, Tamara L., additional, Magi-Galluzzi, Cristina, additional, Osunkoya, Adeboye O., additional, Parwani, Anil V., additional, Robinson, Brian D., additional, Tan, Puay Hoon, additional, Tsuzuki, Toyonori, additional, Yang, Ximing J., additional, Ye, Huihui, additional, and Zhou, Ming, additional

Published
2023
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43. Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper

Author

Berkowitz, Seth J., Kwan, David, Cornish, Toby C., Silver, Elliot L., Thullner, Karen S., Aisen, Alex, Bui, Marilyn M., Clark, Shawn D., Clunie, David A., Eid, Monief, Hartman, Douglas J., Ho, Kinson, Leontiev, Andrei, Luviano, Damien M., O’Toole, Peter E., Parwani, Anil V., Pereira, Nielsen S., Rotemberg, Veronica, Vining, David J., Gaskin, Cree M., Roth, Christopher J., and Folio, Les R.

Published
2022
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44. Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology: Guideline Update From the College of American Pathologists in Collaboration With the American Society for Clinical Pathology and the Association for Pathology Informatics

Author

Evans, Andrew J., Brown, Richard W., Bui, Marilyn M., Chlipala, Elizabeth A., Lacchetti, Christina, Milner, Danny A., Jr., Pantanowitz, Liron, Parwani, Anil V., Reid, Kearin, Riben, Michael W., Reuter, Victor E., Stephens, Stewart, Rachel L., and Thomas, Nicole E.

Subjects
Clinical pathology -- Standards, Practice guidelines (Medicine), Imaging systems -- Standards -- Usage, Imaging technology, Health, American Society of Clinical Pathologists -- Standards, College of American Pathologists -- Standards
Abstract

Context.--The original guideline, 'Validating Whole Slide Imaging for Diagnostic Purposes in Pathology,' was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines. Objective.--To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes. Design.--An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations. Results.--Three strong recommendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems. Conclusions.--Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%., In 2013, the Pathology and Laboratory Quality Center for Evidence-based Guidelines (the Center) of the College of American Pathologists (CAP) released a guideline on the validation of whole slide imaging [...]

Published
2022
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45. A Multimodal Generative AI Copilot for Human Pathology

Author

Lu, Ming Y., primary, Chen, Bowen, additional, Williamson, Drew F. K., additional, Chen, Richard J., additional, Zhao, Melissa, additional, Chow, Aaron K., additional, Ikemura, Kenji, additional, Kim, Ahrong, additional, Pouli, Dimitra, additional, Patel, Ankush, additional, Soliman, Amr, additional, Chen, Chengkuan, additional, Ding, Tong, additional, Wang, Judy J., additional, Gerber, Georg, additional, Liang, Ivy, additional, Le, Long Phi, additional, Parwani, Anil V., additional, Weishaupt, Luca L., additional, and Mahmood, Faisal, additional

Published
2024
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46. Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Author

Elbialy, Abdalla, primary, Kappala, Deepthi, additional, Desai, Dhruv, additional, Wang, Peng, additional, Fadiel, Ahmed, additional, Wang, Shang-Jui, additional, Makary, Mina S., additional, Lenobel, Scott, additional, Sood, Akshay, additional, Gong, Michael, additional, Dason, Shawn, additional, Shabsigh, Ahmad, additional, Clinton, Steven, additional, Parwani, Anil V., additional, Putluri, Nagireddy, additional, Shvets, Gennady, additional, Li, Jenny, additional, and Liu, Xuefeng, additional

Published
2024
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