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5. A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Author

Qiu, Jianwei, Mitra, Jhimli, Ghose, Soumya, Dumas, Camille, Yang, Jun, Sarachan, Brion, and Judson, Marc A.

Subjects
SARCOIDOSIS, CONVOLUTIONAL neural networks, TRANSFORMER models, INTERSTITIAL lung diseases, COMPUTED tomography, PULMONARY fibrosis
Abstract

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images

Author

Karageorgos, Grigorios M., primary, Cho, Sanghee, additional, McDonough, Elizabeth, additional, Chadwick, Chrystal, additional, Ghose, Soumya, additional, Owens, Jonathan, additional, Jung, Kyeong Joo, additional, Machiraju, Raghu, additional, West, Robert, additional, Brooks, James D., additional, Mallick, Parag, additional, and Ginty, Fiona, additional

Published
2024
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7. An Automatic 3-D Ultrasound and Photoacoustic Combined Imaging System for Human Inflammatory Arthritis

Author

Peng, Xiaorui, primary, Dentinger, Aaron, additional, Kewalramani, Shivangi, additional, Xu, Zhanpeng, additional, Gray, Steven, additional, Ghose, Soumya, additional, Tan, Yew Teck, additional, Yang, Zhaoyuan, additional, Jo, Janggun, additional, Chamberland, David, additional, Xu, Guan, additional, Abdulaziz, Nada, additional, Gandikota, Girish, additional, Mills, David, additional, and Wang, Xueding, additional

Published
2023
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9. High-Speed On-Site Deep Learning–Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard

Author

Giannopoulos, Andreas A., primary, Keller, Lukas, additional, Sepulcri, Daniel, additional, Boehm, Reto, additional, Garefa, Chrysoula, additional, Venugopal, Prem, additional, Mitra, Jhimli, additional, Ghose, Soumya, additional, Deak, Paul, additional, Pack, Jed D., additional, Davis, Cynthia L., additional, Stähli, Barbara E., additional, Stehli, Julia, additional, Pazhenkottil, Aju P., additional, Kaufmann, Philipp A., additional, and Buechel, Ronny R., additional

Published
2023
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10. Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI

Author

Ghose, Soumya, Shiradkar, Rakesh, Rusu, Mirabela, Mitra, Jhimli, Thawani, Rajat, Feldman, Michael, Gupta, Amar, Purysko, Andrei, Ponsky, Lee, Madabhushi, Anant, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Descoteaux, Maxime, editor, Maier-Hein, Lena, editor, Franz, Alfred, editor, Jannin, Pierre, editor, Collins, D. Louis, editor, and Duchesne, Simon, editor

Published
2017
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13. High-Speed Onsite Deep-Learning Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as Reference Standard

Author

Giannopoulos, Andreas A, Keller, Lukas, Sepulcri, Daniel, Boehm, Reto, Garefa, Chrysoula, Venugopal, Prem, Mitra, Jhimli, Ghose, Soumya, Deak, Paul, Pack, Jed D, Davis, Cynthia L, Stähli, Barbara E, Stehli, Julia, Pazhenkottil, Aju P, Kaufmann, Philipp A, Buechel, Ronny R, and University of Zurich

Subjects
Radiology, Nuclear Medicine and imaging, 610 Medicine & health, General Medicine, 10181 Clinic for Nuclear Medicine
Published
2023

15. Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

Author

Ghose, Soumya, primary, Cho, Sanghee, additional, Ginty, Fiona, additional, McDonough, Elizabeth, additional, Davis, Cynthia, additional, Zhang, Zhanpan, additional, Mitra, Jhimli, additional, Harris, Adrian L., additional, Thike, Aye Aye, additional, Tan, Puay Hoon, additional, Gökmen-Polar, Yesim, additional, and Badve, Sunil S., additional

Published
2023
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16. Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning

Author

Rivest-Hénault, David, Ghose, Soumya, Pluim, Josien P. W., Greer, Peter B., Fripp, Jurgen, Dowling, Jason A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Menze, Bjoern, editor, Langs, Georg, editor, Montillo, Albert, editor, Kelm, Michael, editor, Müller, Henning, editor, Zhang, Shaoting, editor, Cai, Weidong (Tom), editor, and Metaxas, Dimitris, editor

Published
2014
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20. A Survey of Cervix Segmentation Methods in Magnetic Resonance Images

Author

Ghose, Soumya, Holloway, Lois, Lim, Karen, Chan, Philip, Veera, Jacqueline, Vinod, Shalini K., Liney, Gary, Greer, Peter B., Dowling, Jason, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yoshida, Hiroyuki, editor, Warfield, Simon, editor, and Vannier, Michael W., editor

Published
2013
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21. Multi-atlas and Gaussian Mixture Modeling Based Perirectal Fat Segmentation from CT Images

Author

Ghose, Soumya, Denham, Jim, Ebert, Martin, Kennedy, Angel, Mitra, Jhimli, Rose, Stephen, Dowling, Jason, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yoshida, Hiroyuki, editor, Warfield, Simon, editor, and Vannier, Michael W., editor

Published
2013
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22. Classification Forests and Markov Random Field to Segment Chronic Ischemic Infarcts from Multimodal MRI

Author

Mitra, Jhimli, Bourgeat, Pierrick, Fripp, Jurgen, Ghose, Soumya, Rose, Stephen, Salvado, Olivier, Connelly, Alan, Campbell, Bruce, Palmer, Susan, Sharma, Gagan, Christensen, Soren, Carey, Leeanne, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Shen, Li, editor, Liu, Tianming, editor, Yap, Pew-Thian, editor, Huang, Heng, editor, Shen, Dinggang, editor, and Westin, Carl-Fredrik, editor

Published
2013
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23. A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

Author

Ghose, Soumya, Mitra, Jhimli, Oliver, Arnau, Martí, Robert, Lladó, Xavier, Freixenet, Jordi, Vilanova, Joan C., Comet, Josep, Sidibé, Désiré, Meriaudeau, Fabrice, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, Scheunders, Paul, editor, and Zemčík, Pavel, editor

Published
2012
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24. Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation

Author

Ghose, Soumya, Oliver, Arnau, Martí, Robert, Lladó, Xavier, Freixenet, Jordi, Mitra, Jhimli, Vilanova, Joan C., Comet, Josep, Meriaudeau, Fabrice, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Madabhushi, Anant, editor, Dowling, Jason, editor, Huisman, Henkjan, editor, and Barratt, Dean, editor

Published
2011
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26. Tumor Infiltrating Lymphocytes in Multi-National Cohorts of Ductal Carcinoma In Situ (DCIS) of Breast

Author

Badve, Sunil S., primary, Cho, Sanghee, additional, Lu, Xiaoyu, additional, Cao, Sha, additional, Ghose, Soumya, additional, Thike, Aye Aye, additional, Tan, Puay Hoon, additional, Ocal, Idris Tolgay, additional, Generali, Daniele, additional, Zanconati, Fabrizio, additional, Harris, Adrian L., additional, Ginty, Fiona, additional, and Gökmen-Polar, Yesim, additional

Published
2022
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27. Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Author

Ghose, Soumya, Oliver, Arnau, Martí, Robert, Lladó, Xavier, Freixenet, Jordi, Vilanova, Joan C., Meriaudeau, Fabrice, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Madabhushi, Anant, editor, Dowling, Jason, editor, Yan, Pingkun, editor, Fenster, Aaron, editor, Abolmaesumi, Purang, editor, and Hata, Nobuhiko, editor

Published
2010
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28. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

Author

Litjens, Geert, Toth, Robert, van de Ven, Wendy, Hoeks, Caroline, Kerkstra, Sjoerd, van Ginneken, Bram, Vincent, Graham, Guillard, Gwenael, Birbeck, Neil, Zhang, Jindang, Strand, Robin, Malmberg, Filip, Ou, Yangming, Davatzikos, Christos, Kirschner, Matthias, Jung, Florian, Yuan, Jing, Qiu, Wu, Gao, Qinquan, Edwards, Philip “Eddie”, Maan, Bianca, van der Heijden, Ferdinand, Ghose, Soumya, Mitra, Jhimli, Dowling, Jason, Barratt, Dean, Huisman, Henkjan, and Madabhushi, Anant

Published
2014
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29. Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

Author

Shiradkar, Rakesh, primary, Ghose, Soumya, additional, Mahran, Amr, additional, Li, Lin, additional, Hubbard, Isaac, additional, Fu, Pingfu, additional, Tirumani, Sree Harsha, additional, Ponsky, Lee, additional, Purysko, Andrei, additional, and Madabhushi, Anant, additional

Published
2022
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31. Abstract P6-04-10: Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients from Tissue Microarrays (TMAs)

Author

Ghose Soumya, Yesim Gokmen-Polar, Sanghee Cho, Elizabeth McDonough, Cynthia Davis, Jhimli Mitra, Zhanpan Zhang, Fiona Ginty, and Sunil Badve

Subjects
Cancer Research, Oncology
Abstract

Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients Using Generative Adversarial Network (GAN) Augmented Deep Learning Model Background: DCIS patients have an excellent overall survival rate and over-treatment is always a cause for concern due to potential side-effects. Standard clinicopathological factors (age, growth pattern, tumor size, margin status and grade) have been shown to have limited value in predicting recurrence and segregation of high and low risk patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. In this work, we have developed a deep learning (DL) classification framework that predicts recurrence in DCIS patients from Tissue microarrays (TMAs) hematoxylin and eosin (H&E) images using a generative adversarial network (GAN) augmented deep learning (DL) classification model. A GAN is a class of DL models, in which two adversarial neural networks, generator and discriminator contest among each other to generate high quality images. During the adversarial training process, the generator learns to synthesize realistic images similar to those in the training set while the discriminator learns to distinguish between real and generated images. In recent years, high quality medical images have been generated by GAN models. To the best of our knowledge, this is the first time a GAN model has been used to generate H&E images to train a DL classification model to predict recurrence in DCIS patients. Materials and methods: The cohort was comprised of 68 DCIS patients, aged between 35-89 years, lesion size of 5-90 mm, with a mix of low (15%), intermediate (35%) and 50% high grade cases. Patients were treated with mastectomy and/or a combination of lumpectomy, radiation and hormone therapy. TMAs were constructed from 2mm cores (1-3 cores per patient) in consultation with a breast pathologist to create hematoxylin and eosin (H&E) images for further analysis. The cohort was split into independent training (n=50 patients, 10 with recurrences at 5years) and validation groups (n=18 patients, 6 with recurrences at 5years). TMA (H&E) images were divided into smaller image patches of size 256x256 to train a GAN to generate image patches. A DL classification network (Resnet-Inception v2) was trained using TMA image patches and aggressive image patches generated by GAN to predict recurrence. The ability to generate synthetic image patches of aggressive lesions permitted training of a large DL classification network and predict recurrence in DCIS patients. Importantly, manual annotation was not necessary for the process. Results: The DL classification model trained with both TMA and GAN generated image patches predicted recurrence with an AUC of 0.87, sensitivity of 0.83 and specificity of 0.91 in the validation dataset. The DL classification model trained with image patches from TMAs only predicted recurrence with an AUC of 0.81. Conclusions: The use of a GAN model to generate H&E images circumvents the needs for a large cohort and accurate labor-intensive manual annotation of histopathological images, which is often required for training a large DL classification model. The use of GAN generated aggressive image patches during training significantly improves recurrence prediction accuracy of the DL classification model. Validation in independent larger cohorts is ongoing, and if successful, could provide a novel assay for risk prediction that does not waste precious tissue samples. Citation Format: Ghose Soumya, Yesim Gokmen-Polar, Sanghee Cho, Elizabeth McDonough, Cynthia Davis, Jhimli Mitra, Zhanpan Zhang, Fiona Ginty, Sunil Badve. Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients from Tissue Microarrays (TMAs) [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-10.

Published
2023

34. Human Digital Twin: Automated Cell Type Distance Computation and 3D Atlas Construction in Multiplexed Skin Biopsies

Author

Ghose, Soumya, primary, Ju, Yingnan, additional, McDonough, Elizabeth, additional, Ho, Jonhan, additional, Karunamurthy, Arivarasan, additional, Chadwick, Chrystal, additional, Cho, Sanghee, additional, Rose, Rachel, additional, Corwin, Alex, additional, Surrette, Christine, additional, Martinez, Jessica, additional, Williams, Eric, additional, Sood, Anup, additional, Al-Kofahi, Yousef, additional, Falo, Louis D., additional, Börner, Katy, additional, and Ginty, Fiona, additional

Published
2022
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47. Patient-specific deep deformation models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions

Author

Mitra, Jhimli, primary, MacDonald, Michael, additional, Mills, David, additional, Ghose, Soumya, additional, Smith, L. Scott, additional, Sarcar, Shourya, additional, Yeo, Desmond T. B., additional, Tempany, Clare M. C., additional, Bednarz, Bryan, additional, Jupitz, Sydney A., additional, and Foo, Thomas K. F., additional

Published
2020
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50. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

Author

Algohary, Ahmad, primary, Viswanath, Satish, additional, Shiradkar, Rakesh, additional, Ghose, Soumya, additional, Pahwa, Shivani, additional, Moses, Daniel, additional, Jambor, Ivan, additional, Shnier, Ronald, additional, Böhm, Maret, additional, Haynes, Anne‐Maree, additional, Brenner, Phillip, additional, Delprado, Warick, additional, Thompson, James, additional, Pulbrock, Marley, additional, Purysko, Andrei S, additional, Verma, Sadhna, additional, Ponsky, Lee, additional, Stricker, Phillip, additional, and Madabhushi, Anant, additional

Published
2018
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