Work

Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning

Year: 2023

Type: article

Source: Medical Image Analysis

DOI: 10.1016/j.media.2023.103070

Authors: Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras, Prateek Prasanna

Institutions: Stony Brook University, University of North Carolina at Charlotte

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Stony Brook University, National Institutes of Health, National Cancer Institute

Awards: G1525668854, G1665900764, RI: Medium: Information Super-Resolution for Very Large Images, Methods and Tools for Integrating Pathomics Data into Cancer Registries, Methods and Tools for Integrating Pathomics Data into Cancer Registries, TCIA Sustainment and Scalability - Platforms for Quantitative Imaging Informatics in Precision Medicine, SCH: Blazing Data Trails: Digital Pathology and Specialist Attention, G7130193, Computerized platform for interactive annotation and topological characterization of tumor associated vasculature for predicting response to immunotherapy in lung cancer

OpenAlex ID: W4390322800

FWCI: 1.224

Cites: 73

Cited by: 13

Related to: 10