Zobrazeno 1 - 10
of 1 458
pro vyhledávání: '"Kapse A"'
Autor:
Belagali, Varun, Yellapragada, Srikar, Graikos, Alexandros, Kapse, Saarthak, Li, Zilinghan, Nandi, Tarak Nath, Madduri, Ravi K, Prasanna, Prateek, Saltz, Joel, Samaras, Dimitris
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views of the same image. To perform this view-invariance task, current SSL
Externí odkaz:
http://arxiv.org/abs/2412.01672
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
Digital pathology has advanced significantly over the last decade, with Whole Slide Images (WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High-resolution WSIs are essential for precise diagnosis but technical limit
Externí odkaz:
http://arxiv.org/abs/2408.15218
Publikováno v:
ITM Web of Conferences, Vol 56, p 05004 (2023)
Accidents by not wearing helmet infractions are now a big problem for most emerging nations in the modern, changing world. Both the number of vehicles on the road and the number of traffic law offences are growinPly. Not wearing the helmet enforcemen
Externí odkaz:
https://doaj.org/article/a4faba52c0ae4df794d218584b3064f9
Publikováno v:
ITM Web of Conferences, Vol 44, p 03028 (2022)
Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using fac
Externí odkaz:
https://doaj.org/article/9b319224fa1e43bdae826907d57868e5
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
Autor:
Zhang, Jingwei, Ma, Ke, Kapse, Saarthak, Saltz, Joel, Vakalopoulou, Maria, Prasanna, Prateek, Samaras, Dimitris
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. SAM sh
Externí odkaz:
http://arxiv.org/abs/2307.09570
Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial Networks (GANs) have provided innovative approaches for histopatholog
Externí odkaz:
http://arxiv.org/abs/2304.01053