Work

Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning

Year: 2023

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-031-43993-3_60

Authors: Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras

Institutions: Stony Brook University, Snap (United States), Université Paris-Saclay, CentraleSupélec

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Agence Nationale de la Recherche, Stony Brook University, National Institutes of Health, National Cancer Institute

Awards: RI: Medium: Information Super-Resolution for Very Large Images, G3233243555, Methods and Tools for Integrating Pathomics Data into Cancer Registries, Holistic explainable artificial intelligence schemes for lung cancer prognosis, SCH: Blazing Data Trails: Digital Pathology and Specialist Attention, Computerized platform for interactive annotation and topological characterization of tumor associated vasculature for predicting response to immunotherapy in lung cancer

OpenAlex ID: W4387225572

FWCI: 3.364

Cites: 24

Cited by: 19

Related to: 10