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arXiv Computer Vision By Guibin Zhang, Weier Luo, Lei Cao

Learning to Measure and Diagnose: A Clinical-Dictionary Radiomic Bottleneck Model for Thyroid Nodule Classification

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The paper introduces a clinical‑dictionary radiomic bottleneck model for classifying thyroid nodules from ultrasound images. It combines Family‑Aware Radiomic Learning to estimate measurements across feature families with Fidelity‑Guided Bottleneck Modeling to jointly optimize measurement estimation and classification while preserving measurement fidelity. The model decomposes decision evidence through a linear classifier, mapping feature contributions to clinical dictionary categories, and demonstrates competitive performance on ThyroidXL and TN3K datasets with interpretable case studies.

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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