Saarthak Kapse

33 papers A* 2Journal 20Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SuperDiff: A diffusion super-resolution method for digital pathology with comprehensive quality assessment. Medical Image Anal. Xuan Xu, Saarthak Kapse, Prateek Prasanna
2025	J	`jnl`	Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing. CoRR Saarthak Kapse, Robin Betz, Srinivasan Sivanandan
2025	J	`jnl`	GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology. CoRR Saarthak Kapse, Pushpak Pati, Srikar Yellapragada, Srijan Das, Rajarsi R. Gupta, Joel H. Saltz, Dimitris Samaras, Prateek Prasanna
2025	J	`jnl`	PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology. CoRR Sejuti Majumder, Saarthak Kapse, Moinak Bhattacharya, Xuan Xu, Alisa Yurovsky, Prateek Prasanna
2025	J	`jnl`	PixCell: A generative foundation model for digital histopathology images. CoRR Srikar Yellapragada, Alexandros Graikos, Zilinghan Li, Kostas Triaridis, Varun Belagali, Saarthak Kapse, Tarak Nath Nandi, Ravi K. Madduri, Prateek Prasanna, Tahsin M. Kurç, Rajarsi R. Gupta, Joel H. Saltz, Dimitris Samaras
2025	J	`jnl`	TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning. CoRR Varun Belagali, Saarthak Kapse, Pierre Marza, Srijan Das, Zilinghan Li, Sofiène Boutaj, Pushpak Pati, Srikar Yellapragada, Tarak Nath Nandi, Ravi K. Madduri, Joel H. Saltz, Prateek Prasanna, Stergios Christodoulidis, Maria Vakalopoulou, Dimitris Samaras
2024	J	`jnl`	Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning. Medical Image Anal. Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel H. Saltz, Dimitris Samaras, Prateek Prasanna
2024	—	`conf`	Coca-Mil: Attention-Based Handcrafted-Deep Feature Fusion in Computational Pathology. ISBI Paras Goel, Saarthak Kapse, Pushpak Pati, Prateek Prasanna
2024	J	`jnl`	Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning. CoRR Varun Belagali, Srikar Yellapragada, Alexandros Graikos, Saarthak Kapse, Zilinghan Li, Tarak Nath Nandi, Ravi K. Madduri, Prateek Prasanna, Joel H. Saltz, Dimitris Samaras
2024	J	`jnl`	Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment. CoRR Xuan Xu, Saarthak Kapse, Prateek Prasanna
2024	—	`conf`	HoG-Net: Hierarchical Multi-organ Graph Network for Head and Neck Cancer Recurrence Prediction from CT Images. MICCAI (5) Joseph Bae, Saarthak Kapse, Lei Zhou, Kartik Mani, Prateek Prasanna
2024	A*	`conf`	Learned Representation-Guided Diffusion Models for Large-Image Generation. CVPR Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le, Saarthak Kapse, Prateek Prasanna, Joel H. Saltz, Dimitris Samaras
2024	J	`jnl`	RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency. CoRR Wentao Huang, Meilong Xu, Xiaoling Hu, Shahira Abousamra, Aniruddha Ganguly, Saarthak Kapse, Alisa Yurovsky, Prateek Prasanna, Tahsin M. Kurç, Joel H. Saltz, Michael L. Miller, Chao Chen
2024	A*	`conf`	SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. CVPR Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel H. Saltz, Dimitris Samaras, Rajarsi R. Gupta, Prateek Prasanna
2023	J	`jnl`	Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning. CoRR Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel H. Saltz, Dimitris Samaras, Prateek Prasanna
2023	—	`conf`	CD-Net: Histopathology Representation Learning Using Context-Detail Transformer Network. ISBI Saarthak Kapse, Srijan Das, Prateek Prasanna
2023	J	`jnl`	Learned representation-guided diffusion models for large-image generation. CoRR Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le, Saarthak Kapse, Prateek Prasanna, Joel H. Saltz, Dimitris Samaras
2023	—	`conf`	Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology. IPMI Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Maria Vakalopoulou, Joel H. Saltz, Dimitris Samaras
2023	J	`jnl`	Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning. CoRR Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Joel H. Saltz, Maria Vakalopoulou, Dimitris Samaras
2023	—	`conf`	Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning. MICCAI (8) Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Joel H. Saltz, Maria Vakalopoulou, Dimitris Samaras
2023	—	`conf`	Role of stain normalization in computational pathology: use case in metastatic tissue classification. Digital and Computational Pathology Paras Goel, Saarthak Kapse, Prateek Prasanna
2023	—	`conf`	SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology. ISIC/Care-AI/MedAGI/DeCaF@MICCAI Jingwei Zhang, Ke Ma, Saarthak Kapse, Joel H. Saltz, Maria Vakalopoulou, Prateek Prasanna, Dimitris Samaras
2023	J	`jnl`	SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology. CoRR Jingwei Zhang, Ke Ma, Saarthak Kapse, Joel H. Saltz, Maria Vakalopoulou, Prateek Prasanna, Dimitris Samaras
2023	J	`jnl`	SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. CoRR Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel H. Saltz, Dimitris Samaras, Rajarsi R. Gupta, Prateek Prasanna
2023	—	`conf`	ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology Image Analysis. DGM4MICCAI Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna
2023	J	`jnl`	ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis. CoRR Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna
2022	J	`jnl`	CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network. CoRR Saarthak Kapse, Srijan Das, Prateek Prasanna
2022	J	`jnl`	Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology. CoRR Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Maria Vakalopoulou, Joel H. Saltz, Dimitris Samaras
2022	—	`conf`	Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study. Digital and Computational Pathology Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna
2022	—	`conf`	Subtype-Specific Spatial Descriptors of Tumor-Immune Microenvironment are Prognostic of Survival in Lung Adenocarcinoma. ISBI Saarthak Kapse, Luke Torre-Healy, Richard A. Moffitt, Rajarsi Gupta, Prateek Prasanna
2021	—	`conf`	TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer. IPMI Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna, Chao Chen
2021	J	`jnl`	TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer. CoRR Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna, Chao Chen
2020	J	`jnl`	Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study. CoRR Joseph Bae, Saarthak Kapse, Gagandeep Singh, Tej Phatak, Jeremy Green, Nikhil Madan, Prateek Prasanna

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.