Search Results - "Juyal, Dinkar"

WED-522 - Characterizing the histologic implications of resmetirom-induced liver volume reduction using artificial intelligence-powered digital pathology by Mistry, Pratik, Stanford-Moore, Adam, Egger, Robert, Glickman, Jonathan, Baker, Brian, Chandra, Nidhi, Juyal, Dinkar, Khosla, Archit, Drage, Michael, Resnick, Murray, Wack, Katy, Hennan, Jim, Loomba, Rohit, Harrison, Stephen, Taub, Rebecca, Iyer, Janani

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Characterizing the histologic implications of resmetirom-induced liver volume reduction using artificial intelligence-powered digital pathology by Mistry, Pratik, Stanford-Moore, Adam, Egger, Robert, Glickman, Jonathan, Baker, Brian, Chandra, Nidhi, Juyal, Dinkar, Khosla, Archit, Drage, Michael, Resnick, Murray, Wack, Katy, Hennan, Jim, Loomba, Rohit, Harrison, Stephen, Taub, Rebecca, Iyer, Janani

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A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis by Bosch, Jaime, Chung, Chuhan, Carrasco‐Zevallos, Oscar M., Harrison, Stephen A., Abdelmalek, Manal F., Shiffman, Mitchell L., Rockey, Don C., Shanis, Zahil, Juyal, Dinkar, Pokkalla, Harsha, Le, Quang Huy, Resnick, Murray, Montalto, Michael, Beck, Andrew H., Wapinski, Ilan, Han, Ling, Jia, Catherine, Goodman, Zachary, Afdhal, Nezam, Myers, Robert P., Sanyal, Arun J.

“…Background and Aims The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We…”
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AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases by Iyer, Janani S., Juyal, Dinkar, Le, Quang, Shanis, Zahil, Pokkalla, Harsha, Pouryahya, Maryam, Pedawi, Aryan, Stanford-Moore, S. Adam, Biddle-Snead, Charles, Carrasco-Zevallos, Oscar, Lin, Mary, Egger, Robert, Hoffman, Sara, Elliott, Hunter, Leidal, Kenneth, Myers, Robert P., Chung, Chuhan, Billin, Andrew N., Watkins, Timothy R., Patterson, Scott D., Resnick, Murray, Wack, Katy, Glickman, Jon, Burt, Alastair D., Loomba, Rohit, Sanyal, Arun J., Glass, Ben, Montalto, Michael C., Taylor-Weiner, Amaro, Wapinski, Ilan, Beck, Andrew H.

“…Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for…”
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Machine learning identifies histologic features associated with regression of cirrhosis in treatment for chronic hepatitis B by Juyal, Dinkar, Shukla, Chinmay, Pokkalla, Harsha, Taylor, Amaro, Zevallos, Oscar, Resnick, Murray, Montalto, Michael, Beck, Andrew, Wapinski, Ilan, Marcellin, Patrick, Flaherty, John F., Suri, Vithika, Gaggar, Anuj, Subramanian, Mani, Jacobson, Ira, Gane, Edward, Buti, Maria

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LBP31 - Machine learning identifies histologic features associated with regression of cirrhosis in treatment for chronic hepatitis B by Juyal, Dinkar, Shukla, Chinmay, Pokkalla, Harsha, Taylor, Amaro, Zevallos, Oscar, Resnick, Murray, Montalto, Michael, Beck, Andrew, Wapinski, Ilan, Marcellin, Patrick, Flaherty, John F., Suri, Vithika, Gaggar, Anuj, Subramanian, Mani, Jacobson, Ira, Gane, Edward, Buti, Maria

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Abstract PO3-07-04: Prediction of PAM50 molecular subtypes from H&E-stained breast cancer specimens using tumor microenvironment features and additive multiple instance learning models by Guramare, Maria, Javed, Syed Ashar, Kirkup, Christian, Juyal, Dinkar, Brosnan-Cashman, Jacqueline, Mountain, Victoria, Leung, Ryan, Rahsepar, Bahar, Abel, John, Taylor-Weiner, Amaro, Conway, Jake

“…Abstract Background: PAM50, a 50-gene signature, classifies breast cancers into one of five subtypes (basal, luminal A, luminal B, HER2-enriched, and…”
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Abstract 7402: Foundation AI models predict molecular measurements of tumor purity by Gerardin, Ylaine, Shenker, Daniel, Hipp, Jennifer, Harguindeguy, Natalia, Juyal, Dinkar, Shah, Chintan, Javed, Syed Ashar, Thibault, Marc, Nercessian, Michael, Sanghavi, Darpan, Trotter, Benjamin, Leung, Ryan

“…Abstract Background: Molecular assays, such as comprehensive genomic profiling, play a critical role in characterizing patient disease and guiding treatment…”
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SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology by Juyal, Dinkar, Shingi, Siddhant, Javed, Syed Ashar, Padigela, Harshith, Shah, Chintan, Sampat, Anand, Khosla, Archit, Abel, John, Taylor-Weiner, Amaro

“…Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images…”
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Abstract PO2-14-12: Accurate quantification of slide-level HER2 scores in breast cancer using a machine-learning model, AIM-HER2 Breast Cancer by Shanis, Zahil, Cabeen, Ryan, Chakraborty, Shreya, Shamshoian, John, Thibault, Marc, Padigela, Harshith, Juyal, Dinkar, Javed, Syed Ashar, Qian, William, Kim, Juhyun, Rucker, Beckett, Brosnan-Cashman, Jacqueline, Pokkalla, Harsha, Mehta, Jimish, Taylor-Weiner, Amaro, Glass, Ben, Balasubramanian, Santhosh

“…Abstract Background: HER2 expression level is a key factor in determining the optimal treatment course for breast cancer patients. Roughly 15% of breast…”
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Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities by Le, Nhat, Shen, Ciyue, Shah, Chintan, Martin, Blake, Shenker, Daniel, Padigela, Harshith, Hipp, Jennifer, Grullon, Sean, Abel, John, Pokkalla, Harsha Vardhan, Juyal, Dinkar

“…Mechanistic interpretability has been explored in detail for large language models (LLMs). For the first time, we provide a preliminary investigation with…”
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Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology by Javed, Syed Ashar, Juyal, Dinkar, Padigela, Harshith, Taylor-Weiner, Amaro, Yu, Limin, Prakash, Aaditya

“…Multiple Instance Learning (MIL) has been widely applied in pathology towards solving critical problems such as automating cancer diagnosis and grading,…”
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SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology by Juyal, Dinkar, Shingi, Siddhant, Javed, Syed Ashar, Padigela, Harshith, Shah, Chintan, Sampat, Anand, Khosla, Archit, Abel, John, Taylor-Weiner, Amaro

“…Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images…”
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Rethinking Machine Learning Model Evaluation in Pathology by Javed, Syed Ashar, Juyal, Dinkar, Shanis, Zahil, Chakraborty, Shreya, Pokkalla, Harsha, Prakash, Aaditya

“…Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes. However, standard ML models often lack the…”
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Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts by Li, Jin, Rajan, Deepta, Shah, Chintan, Juyal, Dinkar, Chakraborty, Shreya, Akiti, Chandan, Kos, Filip, Iyer, Janani, Sampat, Anand, Behrooz, Ali

“…Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires…”
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ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology by Nguyen, Tan H, Juyal, Dinkar, Li, Jin, Prakash, Aaditya, Nofallah, Shima, Shah, Chintan, Gullapally, Sai Chowdary, Yu, Limin, Griffin, Michael, Sampat, Anand, Abel, John, Lee, Justin, Taylor-Weiner, Amaro

“…Differences in staining and imaging procedures can cause significant color variations in histopathology images, leading to poor generalization when deploying…”
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PLUTO: Pathology-Universal Transformer by Juyal, Dinkar, Padigela, Harshith, Shah, Chintan, Shenker, Daniel, Harguindeguy, Natalia, Liu, Yi, Martin, Blake, Zhang, Yibo, Nercessian, Michael, Markey, Miles, Finberg, Isaac, Luu, Kelsey, Borders, Daniel, Javed, Syed Ashar, Krause, Emma, Biju, Raymond, Sood, Aashish, Ma, Allen, Nyman, Jackson, Shamshoian, John, Chhor, Guillaume, Sanghavi, Darpan, Thibault, Marc, Yu, Limin, Najdawi, Fedaa, Hipp, Jennifer A, Fahy, Darren, Glass, Benjamin, Walk, Eric, Abel, John, Pokkalla, Harsha, Beck, Andrew H, Grullon, Sean

“…Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images…”
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Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology by Gullapally, Sai Chowdary, Zhang, Yibo, Mittal, Nitin Kumar, Kartik, Deeksha, Srinivasan, Sandhya, Rose, Kevin, Shenker, Daniel, Juyal, Dinkar, Padigela, Harshith, Biju, Raymond, Minden, Victor, Maheshwari, Chirag, Thibault, Marc, Goldstein, Zvi, Novak, Luke, Chandra, Nidhi, Lee, Justin, Prakash, Aaditya, Shah, Chintan, Abel, John, Fahy, Darren, Taylor-Weiner, Amaro, Sampat, Anand

“…Machine learning algorithms have the potential to improve patient outcomes in digital pathology. However, generalization of these tools is currently limited by…”
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