Search Results for author: Saarthak Kapse

Found 10 papers, 6 papers with code

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

1 code implementation CVPR 2024 Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel Saltz, Dimitris Samaras, Rajarsi R. Gupta, Prateek Prasanna

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.

Multiple Instance Learning

SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology

no code implementations12 Jul 2023 Jingwei Zhang, Ke Ma, Saarthak Kapse, Joel Saltz, Maria Vakalopoulou, Prateek Prasanna, Dimitris Samaras

On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5. 07% to 5. 12% in Dice score and 4. 50% to 8. 48% in IOU.

Instance Segmentation Segmentation +1

ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis

no code implementations3 Apr 2023 Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna

This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images.

Denoising Image Generation

Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning

1 code implementation21 Mar 2023 Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras

Compared to conventional full fine-tuning approaches, we fine-tune less than 1. 3% of the parameters, yet achieve a relative improvement of 1. 29%-13. 61% in accuracy and 3. 22%-27. 18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.

Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology

1 code implementation23 Dec 2022 Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Maria Vakalopoulou, Joel Saltz, Dimitris Samaras

Our method outperforms previous dense matching methods by up to 7. 2% in average precision for detection and 5. 6% in average precision for instance segmentation tasks.

Contrastive Learning Instance Segmentation +2

CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network

1 code implementation28 Mar 2022 Saarthak Kapse, Srijan Das, Prateek Prasanna

To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net).

Representation Learning

TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

1 code implementation13 May 2021 Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna, Chao Chen

Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures.

Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

no code implementations15 Jul 2020 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

Radiomic and DL classification models had mAUCs of 0. 78+/-0. 02 and 0. 81+/-0. 04, compared with expert scores mAUCs of 0. 75+/-0. 02 and 0. 79+/-0. 05 for mechanical ventilation requirement and mortality prediction, respectively.

Decision Making Mortality Prediction

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