231 citations · 504 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology
Wenhui Wang, Shuming Ma, Hanwen Xu +4
This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner. Specifically, we split the gigapixel image into a sequence o…