Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Saarthak"'
Autor:
Lakshmi Madhuri Peddada, Phyu Phyu Cho, Saarthak Dulgaj, Ratnamala Annapragada, Phani Raja Kanuparthy
Publikováno v:
Results in Optics, Vol 13, Iss , Pp 100537- (2023)
Copper oxide/cuprous oxide and carbon heterostructured nanocomposites (CuO/Cu2O-C NCs) were fabricated using an agro-biomass wheatgrass extract (WGE), presenting an innovative, eco-friendly, and facile green synthetic approach. The CuO/Cu2O-C nanocom
Externí odkaz:
https://doaj.org/article/fa8ff098ad7d44abb50a4d05cd42d594
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
Autor:
Maria Jesus Herrero, Dmitry Velmeshev, David Hernandez-Pineda, Saarthak Sethi, Shawn Sorrells, Payal Banerjee, Catherine Sullivan, Abha R. Gupta, Arnold R. Kriegstein, Joshua G. Corbin
Publikováno v:
Molecular Autism, Vol 11, Iss 1, Pp 1-14 (2020)
Abstract Background Studies of individuals with autism spectrum disorder (ASD) have revealed a strong multigenic basis with the identification of hundreds of ASD susceptibility genes. ASD is characterized by social deficits and a range of other pheno
Externí odkaz:
https://doaj.org/article/4156d40f162a400b9104025a043197f0
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
Autor:
Joseph Bae, Saarthak Kapse, Gagandeep Singh, Rishabh Gattu, Syed Ali, Neal Shah, Colin Marshall, Jonathan Pierce, Tej Phatak, Amit Gupta, Jeremy Green, Nikhil Madan, Prateek Prasanna
Publikováno v:
Diagnostics, Vol 11, Iss 10, p 1812 (2021)
In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentif
Externí odkaz:
https://doaj.org/article/0c652dec96d14cfab0de3ea10dae1243
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
Autor:
Zhang, Jingwei, Kapse, Saarthak, Ma, Ke, Prasanna, Prateek, Saltz, Joel, Vakalopoulou, Maria, Samaras, Dimitris
Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-learning methods. Current state of the art methods are based on multi-inst
Externí odkaz:
http://arxiv.org/abs/2303.12214