Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Juyal, Dinkar"'
Autor:
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 similar interpretability methods for medical imaging. Specifically, we analyze the features fro
Externí odkaz:
http://arxiv.org/abs/2407.10785
Autor:
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 provide a unique challenge for computer-vision-based analysis: a single pathology Whole Sli
Externí odkaz:
http://arxiv.org/abs/2405.07905
Autor:
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 deep-learning models trained from a different data source. Various color augmentation method
Externí odkaz:
http://arxiv.org/abs/2306.04527
Autor:
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 sensitivity to variations in tissue preparation, staining procedures and scanning equipmen
Externí odkaz:
http://arxiv.org/abs/2305.02401
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
Autor:
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. Machine learning problems in medical imaging often deal with rare diseases, making it import
Externí odkaz:
http://arxiv.org/abs/2303.13405
Autor:
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 highly specialized pathologists, making it expensive and time-consuming. Self-training can alle
Externí odkaz:
http://arxiv.org/abs/2211.07692
Autor:
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, predicting patient prognosis, and therapy response. Deploying these models in a clinical setting
Externí odkaz:
http://arxiv.org/abs/2206.01794
Autor:
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 rigorous evaluation required for clinical decisions. Machine learning techniques for natural i
Externí odkaz:
http://arxiv.org/abs/2204.05205
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Akademický článek
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