16 citations · 34 across the 8 of their papers we have counts for
6 papers · 1 filter
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 +10
Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal unde…
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…
BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once
Theodore Zhao, Yu Gu, Jianwei Yang +12
Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. Holistic image analysis comprises interd…
Foundation Models for Biomedical Image Segmentation: A Survey
Ho Hin Lee, Yu Gu, Theodore Zhao +9
Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for genera…
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Sheng Zhang, Yanbo Xu, Naoto Usuyama +21
Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process differe…