Semi-supervised Learning with Robust Loss in Brain Segmentation

2022/12/03 by Hedong Zhang, Zhang, Hedong, Anand A. Joshi +1
Computer Science · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2212.03082

openalex publication_date 2022/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images. The semi-supervised model uses less labeled data, and the performance is competitive with the supervised model with full labeled data. This framework could reduce the cost of labeling MRI images. We also introduced robust loss to reduce the noise effects of inaccurate labels generated in semi-supervised learning.

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