Thyroid Nodule Detection

Latest papers

9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Learning to Measure and Diagnose: A Clinical-Dictionary Radiomic Bottleneck Model for Thyroid Nodule Classification
Oct 21, 2026Guibin Zhang, Weier Luo, Lei CaoThyroid Nodule Detection Medical Image Classification
School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction
Oct 9, 2026Laura Testa, Pierpaolo Brutti, Umberto Ferraro PetrilloThyroid Nodule Detection Medical Image Classification
Department of Statistical Sciences, Sapienza University of Rome, Italy.
CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
Aug 14, 2026Bingxin Yu, Xueli Wang, Jerry Zhou +6Thyroid Nodule Detection Ultrasound Image Classification
China-Japan Union Hospital of Jilin University, Changchun, 130012, P.R. China. · College of Computer Science and Technology, Jilin University, Changchun, 130012, P.R. China. · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, P.R. China. +3
Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting
Aug 12, 2026Haifan Gong, Shiyu Chen, Bodong Wang +11Radiology Report Generation Clinical Decision Support
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · School of Software Engineering, Sun Yat-sen University, Zhuhai, China · Independent Researcher, Jersey City, NJ, USA +6
Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation
Jul 13, 2026Md. Sadibul Hasan Sadib, Md. Mohayminul Mukit, Rahmatul Kabir Rasel Sarker +2Probability Calibration Selective Prediction
Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh. · Department of Computer Science and Engineering, Daffodil International University, Savar, Dhaka, Bangladesh. · Department of Public Health, Daffodil International University, Dhaka, Bangladesh. +1
An Approach for Thyroid Nodule Analysis Using Thermographic Images
May 28, 2026J. R. González, É. O. Rodrigues, C. P. Damião +6Thermal Imaging Medical Image Analysis
Computer Science Department, Universidade Federal Fluminense, Av. Milton Tavares de Souzas, s/n—Boa Viagem, Niterói, RJ 24210-330, Brazil · Radiology Department, Hospital Universitário Antônio Pedro (HUAP), Av. Marquês do Paraná, 303, Niterói, RJ, Brazil · Applied Computation Group NCA-UFMA, Universidade Federal do Maranhão, Av. dos Portugueses, São Luís, 1966, MA, Brazil +2
Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
Mar 30, 2026Yangmei Chen, Zhongyuan Zhang, Xikun Zhang +4Thyroid Nodule Detection Ultrasound Image Classification
Guangdong Institute of Smart Education, Jinan University, Guangzhou 510632, China
Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift
Mar 9, 2026Maziar Sabouri, Nourhan Bayasi, Arman RahmimThyroid Nodule Detection Ultrasound Image Segmentation
Department of Astronomy and Physics, University of British Columbia, Vancouver, Canada · Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, Canada · Department of Radiology, University of British Columbia, Vancouver, Canada +1
Effectiveness of Automatically Curated Dataset in Thyroid Nodules Classification Algorithms Using Deep Learning
Feb 1, 2026Jichen Yang, Jikai Zhang, Benjamin Wildman-Tobriner +1Thyroid Nodule Detection Ultrasound Image Classification
Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA · Department of Radiology, Duke University Medical Center, Durham, NC, USA

Oct 21, 2026J/K move · Enter open · S save

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

Guibin Zhang, Weier Luo, Lei Cao

School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Thyroid Nodule Detection Medical Image Classification Explainable Medical Image Analysis Radiomics Concept Bottleneck Models

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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.