Work

HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis

Year: 2024

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-031-72083-3_15

Authors: Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Juming Xiong, Shunxing Bao, Hao Li, Mengmeng Yin, Yu Wang, Shilin Zhao, Yucheng Tang, Haichun Yang, Yuankai Huo

Institutions: Vanderbilt University, Vanderbilt University Medical Center, Nvidia (United States)

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Vanderbilt University, Leona M. and Harry B. Helmsley Charitable Trust, Nvidia, National Institutes of Health, National Institute of Mental Health, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Biomedical Imaging and Bioengineering

Awards: G1296419534, G2252422194, NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3), G3514670832, G412688864, fMRI physiological signatures of aging and Alzheimer's Disease, G517629734, G5235705212, G5993123900, G6573109839, G6665030671, G6718589257, G7623027640, G9000035539, G9000041293, G9000167524, Dual-wavelength endoscopic Raman probe for eosinophilic esophagitis, Complementary ex vivo multimodal optical imaging and in vivo Raman spectroscopy to understand tissue dynamics

OpenAlex ID: W4403365624

FWCI: 2.43

Cites: 34

Cited by: 8

Related to: 10