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20242026
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cs.CV2026

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao +27

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning trans…

cs.CV2026

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

Hyunjae Kim, Dain Kim, Pan Xiao +25

Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by…

cs.CV2026

Video Models Can Reason with Verifiable Rewards

Tinghui Zhu, Sheng Zhang, James Y. Huang +5

Video diffusion models have made rapid progress in perceptual realism and temporal coherence, but they remain primarily optimized for plausible generation rather than verifiable re…

cs.CV2026

Learning Sparse Visual Representations via Spatial-Semantic Factorization

Theodore Zhengde Zhao, Sid Kiblawi, Jianwei Yang +6

Self-supervised learning (SSL) faces a fundamental conflict between semantic understanding and image reconstruction. High-level semantic SSL (e.g., DINO) relies on global tokens th…

cs.CV2026

Scaling medical imaging report generation with multimodal reinforcement learning

Qianchu Liu, Sheng Zhang, Guanghui Qin +11

Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal unde…

cs.CV2025

Boltzmann Attention Sampling for Image Analysis with Small Objects

Theodore Zhao, Sid Kiblawi, Naoto Usuyama +4

Detecting and segmenting small objects, such as lung nodules and tumor lesions, remains a critical challenge in image analysis. These objects often occupy less than 0.1% of an imag…