Newest papers

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

Guibin Zhang, Weier Luo, Lei Cao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29738 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Ultrasound tiroid mendukung penilaian nodular malignancy, tetapi prediksi klasifikasi sering tidak memiliki bukti yang terkait dengan deskripsi klinis yang akrab. Radiomics menyediakan pengukuran yang jelas yang dapat mendukung penjelasan tersebut melalui kamus klinis. Namun, menggunakan pengukuran ini sebagai bukti yang dapat ditafsirkan membutuhkan perkiraan yang dapat diandalkan dari gambar ultrasound dan pemeliharaan kesetiaan pengukuran saat mengoptimalkan kinerja klasifikasi. Kami mengusulkan model bottleneck radiomik klinis-diktator untuk klasifikasi nodul tiroid. Dalam model ini, Family-Aware Radiomic Learning (FARL) memperkirakan pengukuran dengan pengawasan seimbang di seluruh keluarga fitur. Fidelity-Guided Bottleneck Modeling (FGBM) membangun bottleneck tetap, diikuti oleh optimasi bersama dari

Machine translation (Meta M2M100); the English original follows.

Abstract

Thyroid ultrasound supports the assessment of nodule malignancy, but classification predictions often lack evidence linked to familiar clinical descriptions. Radiomics provides explicitly defined measurements that can support such explanations through a clinical dictionary. However, using these measurements as interpretable evidence requires reliable estimation from ultrasound images and preservation of measurement fidelity when optimizing classification performance. We propose a clinical-dictionary radiomic bottleneck model for thyroid nodule classification. Within this model, Family-Aware Radiomic Learning (FARL) estimates measurements with balanced supervision across feature families. Fidelity-Guided Bottleneck Modeling (FGBM) constructs a fixed bottleneck, followed by joint optimization of measurement estimation and classification under fidelity guidance. Decision evidence decomposition then traces feature contributions through a linear classifier and organizes them by clinical dictionary categories. Experiments on ThyroidXL and TN3K show competitive classification performance, while case studies illustrate feature contributions for benign and malignant nodules. These findings indicate interpretable classification with inspectable quantitative evidence informed by clinical knowledge. Code is available online.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Artificial intelligence, Classifier (UML), Bottleneck, Computer science, Feature (linguistics), Pattern recognition (psychology)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange