mlsanityData to 7 Oct 2026

Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction

arXiv 2603.11206 ↗First posted 11 Mar 2026cs.CVcited by 1

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Abstract (arXiv)

Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 65 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

32 of 65 references.

References · 32

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20 more
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026ELDiff: When Evidential Learning Meets Text-to-Image Diffusioncited by 0