Zobrazeno 1 - 10
of 969
pro vyhledávání: '"Saltz, Joel"'
Autor:
Huang, Wentao, Xu, Meilong, Hu, Xiaoling, Abousamra, Shahira, Ganguly, Aniruddha, Kapse, Saarthak, Yurovsky, Alisa, Prasanna, Prateek, Kurc, Tahsin, Saltz, Joel, Miller, Michael L., Chen, Chao
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due
Externí odkaz:
http://arxiv.org/abs/2411.15076
Autor:
Hasan, Mahmudul, Hu, Xiaoling, Abousamra, Shahira, Prasanna, Prateek, Saltz, Joel, Chen, Chao
Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing the latent space into interpretable subspaces. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2410.02012
Autor:
Bhawsar, Praphulla MS, Ramin, Cody, Lenz, Petra, Duggan, Máire A, Harris, Alexandra R, Jenkins, Brittany, Cora, Renata, Abubakar, Mustapha, Gierach, Gretchen, Saltz, Joel, Almeida, Jonas S
Crown-like structures (CLS) in breast adipose tissue are formed as a result of macrophages clustering around necrotic adipocytes in specific patterns. As a histologic marker of local inflammation, CLS could have potential diagnostic utility as a biom
Externí odkaz:
http://arxiv.org/abs/2409.08275
Introduction: Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability.
Externí odkaz:
http://arxiv.org/abs/2409.03080
Autor:
Le, Minh-Quan, Graikos, Alexandros, Yellapragada, Srikar, Gupta, Rajarsi, Saltz, Joel, Samaras, Dimitris
Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image domains such as digital histopathology and remote sensing. Existing m
Externí odkaz:
http://arxiv.org/abs/2407.14709
Autor:
Chakraborty, Souradeep, Perez, Dana, Friedman, Paul, Sheuka, Natallia, Friedman, Constantin, Yaskiv, Oksana, Gupta, Rajarsi, Zelinsky, Gregory J., Saltz, Joel H., Samaras, Dimitris
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading. We engage this decoding task by developing a novel method for predicting the attention of pathologists as they rea
Externí odkaz:
http://arxiv.org/abs/2403.17255
Autor:
Rashidian, Sina, Abell-Hart, Kayley, Hajagos, Janos, Moffitt, Richard, Lingam, Veena, Garcia, Victor, Tsai, Chao-Wei, Wang, Fusheng, Dong, Xinyu, Sun, Siao, Deng, Jianyuan, Gupta, Rajarsi, Miller, Joshua, Saltz, Joel, Saltz, Mary
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 12, p e22649 (2020)
BackgroundDiabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are th
Externí odkaz:
https://doaj.org/article/3fbf90bf38164f62b4213548879c07e7
Autor:
Kapse, Saarthak, Pati, Pushpak, Das, Srijan, Zhang, Jingwei, Chen, Chao, Vakalopoulou, Maria, Saltz, Joel, Samaras, Dimitris, Gupta, Rajarsi R., Prasanna, Prateek
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides. Traditionally, MIL interpretability is limited to identifying sali
Externí odkaz:
http://arxiv.org/abs/2312.15010
Autor:
Graikos, Alexandros, Yellapragada, Srikar, Le, Minh-Quan, Kapse, Saarthak, Prasanna, Prateek, Saltz, Joel, Samaras, Dimitris
To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure the painstaking patch-level annotation effort required in specialized domains like histopath
Externí odkaz:
http://arxiv.org/abs/2312.07330
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Autor:
Kapse, Saarthak, Das, Srijan, Zhang, Jingwei, Gupta, Rajarsi R., Saltz, Joel, Samaras, Dimitris, Prasanna, Prateek
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations o
Externí odkaz:
http://arxiv.org/abs/2309.06439