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arXiv Computer Vision By Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca, Andre Gustavo Hochuli

Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

Read the original on arXiv Computer Vision →

The paper introduces a method for generalizable brain tumor segmentation in the BraTS 2026 Challenge. It builds on the nnU-Net framework with a large residual encoder, adding semi‑supervised learning via pseudo‑labels and a tumor‑aware deformable augmentation that locally deforms lesions while preserving surrounding anatomy. The approach improves Dice and NSD scores across all tumor regions compared to labeled‑only baselines, demonstrating the complementary benefits of self‑training and the proposed augmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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