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arXiv Computer Vision By Hrishikesh Singh, Aarti Sharma, Divyanshu Kumar, Millie Pant

Reading the Mask: Descriptor-Grounded BI-RADS Rationale Generation for Breast MRI Segmentation

Read the original on arXiv Computer Vision →

The paper presents a method that derives BI‑RADS descriptors from breast MRI segmentation masks and uses a large language model to generate natural‑language rationales grounded in those descriptors. A BI‑RADS decision tree with eight quantitative radiomic measurements, lesion size, and component count informs the descriptors, which are then verbalised by the model. Applying this framework to 1,506 cases in the MAMA‑MIA dataset produced 3,012 reproducible BI‑RADS rationales with high faithfulness (99.98%) and counterfactual accuracy (99.3%).

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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