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PatchLoc: Embedded patch localization pretext task for tumor segmentation in medical images
Date
2024
Abstract
Supervised deep learning methods have produced state-of-the-art results with large labeled datasets. However, accessing large labeled datasets is difficult in medical image analysis because of a shortage of medical experts, expensive annotations, and privacy constraints in the healthcare domain. self-supervised learning is a branch of machine learning that exploits unlabeled data to encourage network weights toward a valid latent representation of the data during a so-called pretext task. The features learned by the model while solving pretext tasks are transferred to a downstream task where limited annotations are available. In this work, we propose PatchLoc, a novel pretext task whose objective is to find the location of a given patch from an image as a source of supervision. We validated the effectiveness of PatchLoc on a downstream segmentation task using three different medical datasets. PatchLoc yields substantial improvements compared to U-Net trained from scratch and other pretext task-based approaches in a low data regime.
Supervisor
Description
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Access, 2024, 12, pp. 66845-66857
Files
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Cheke_2024_Patchloc.pdf
Adobe PDF, 3.64 MB