5 papers
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…
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…
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Cliff Wong, Sam Preston, Qianchu Liu +22
A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text…
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
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…