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

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.

Methods

Our method inputs a small dataset of labeled scans and a large dataset of unlabeled scans and outputs a validated labeled dataset used to train a supervised model (nnU-Net). The estimated correction effort is reduced by having the radiologist correct a subset of the scan labels computed by a few-shot learning model (UniverSeg). The method u

SUBMITTER: Ryabtsev A 

PROVIDER: S-EPMC12476431 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature