Streamlining the annotation process by radiologists of volumetric medical images with few-shot learning.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jun 25, 2025

Abstract

PURPOSE: Radiologist's manual annotations limit robust deep learning in volumetric medical imaging. While supervised methods excel with large annotated datasets, few-shot learning performs well for large structures but struggles with small ones, such as lesions. This paper describes a novel method that leverages the advantages of both few-shot learning models and fully supervised models while reducing the cost of manual annotation.

Authors

  • Alina Ryabtsev
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Richard Lederman
    Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
  • Jacob Sosna
    Department of Radiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

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