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PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology
Fanqi Cheng, Kuo Gong, Shangke Liu +15
Pathology vision-language models (VLMs) have shown strong visual perception ability, but their robustness in the language domain remains poorly characterized. Existing pathology VL…
Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics
Zheyu Zhu, Junchao Zhu, Fengbei Liu +7
Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions. Most activ…
Seeing Abnormal from Normal: Glomerular Abnormality in Representations of Normal Renal Morphology
Greta Hasko, Rachit Saluja, Tianyu Shi +13
Fine-grained evaluation of glomerular pathology must distinguish normal glomeruli from abnormalities such as global and segmental glomerulosclerosis, obsolescent, ischemic, solidif…
Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Events
Paula Feldman, Nusrat Binta Nizam, Sunwoo Kwak +3
Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables that may be missing, outdated, or…
StainBridge: Stain-Aware Pairwise Registration of Serial Renal Biopsy Whole-Slide Images Across Structural and Immunohistochemical Stains
Ellen Wei, Bohang Jiang, Yanfan Zhu +8
Three-dimensional (3D) reconstruction of histopathology tissue requires accurate pairwise registration of serial whole-slide images (WSIs). Cross-stain benchmarks have advanced reg…
ShapKO: Shapley-Adaptive Modality Knockout for Robust Multimodal Learning
Nusrat Binta Nizam, Fengbei Liu, Sunwoo Kwak +3
Multimodal medical models often degrade when inputs are missing, a common scenario in real-world clinical workflows. Separately, even when all modalities are present, modality domi…
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…
MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations
Leiyue Zhao, Tianyu Shi, Daniel Reisenbuchler +12
Instance-level quantification of kidney functional units is essential for morphometric analysis, yet most publicly available pathology datasets provide only semantic segmentation a…
Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
Leo Milecki, Qingyu Hu, Bahram Jafrasteh +2
Masked autoencoders (MAEs) have recently shown promise for self-supervised representation learning of resting-state brain functional connectivity (FC). However, a fundamental quest…
Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy
Jorge Tapias Gomez, Despoina Kanata, Aneesh Rangnekar +8
Clinical trial studies indicate benefit of watch-and-wait (WW) surveillance for patients with rectal cancer showing a complete or near clinical response (CR) directly after treatme…
Spectral Vision Transformer for Efficient Tokenization with Limited Data
Alexandra G. Roberts, Maneesh John, Jinwei Zhang +10
We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical prope…
HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis
Fengbei Liu, Sunwoo Kwak, Hao Phung +6
Non-contrast chest CTs offer a rich opportunity for both conventional pulmonary and opportunistic extra-pulmonary screening. While Multi-Task Learning (MTL) can unify these diverse…
Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations
Heejong Kim, Abhishek Thanki, Roel van Herten +2
Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore…
AI-Based Detection of Temporal Changes in MR-Linac Images Acquired During Routine Prostate Radiotherapy
Seungbin Park, Peilin Wang, Ryan Pennell +6
Purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy and explore the broader potential of MRL…