The paper presents a radiomics-based approach for classifying thyroid nodules using ultrasound images. It extracts quantitative features with PyRadiomics and employs a Generalized Additive Model (GAM) chosen for its balance of performance and interpretability, achieving an AUC of 0.89, sensitivity of 0.83, and specificity of 0.82 on a public dataset. To provide uncertainty estimates, Cross-Conformal Prediction with label-conditional guarantees is applied, allowing 63% of test nodules to receive a confident single-label prediction while the remaining 37% are referred to clinicians.
By Laura Testa, Pierpaolo Brutti, Umberto Ferraro Petrillo
arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).
By Bingxin Yu, Xueli Wang, Jerry Zhou, Wenyan Wang, Li Wen, Lan Huang, Xin Feng, Fengfeng Zhou, Kewei Li
arXiv:2607. 12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift.
By Md. Sadibul Hasan Sadib, Md. Mohayminul Mukit, Rahmatul Kabir Rasel Sarker, Tahmid Alam Tamim, Md. Monir Hossain Shimul
TAM-Chain is a multi‑scale thyroid cytology classification framework that uses Absorbing Markov Chains and Shannon Entropy to quantify uncertainty and dynamically decide when to stop processing and refer to a specialist. It processes images at 10×, 20×, and 40× magnifications, achieving a Macro F1 score of 0.9741 on an internal test set with a 0 % false‑negative rate, and maintains a Macro F1 of 0.7026 on an external validation set with severe domain shift. The method outperforms single‑magnification baselines by adaptively adjusting stopping steps and triggering specialist referrals, thereby reducing critical diagnostic errors.
By Hai Pham Ngoc
Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models.
arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.
By Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon
arXiv:2608. 12590v1 Announce Type: new Abstract: Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and provide limited support for clinical review.
By Haifan Gong, Shiyu Chen, Bodong Wang, Yuqi Wang, Shijie Wang, Guoliang You, Xinyu Xiong, Haowei Wang, Mingzhi Mao, Dexing Kong, Qinghua Liu, Wei Lou, Fei Chen, Guanbin Li
This review examines the use of artificial intelligence and radiomics for diagnosing thyroid cancer, summarizing 42 studies that demonstrate the effectiveness of ultrasound-based radiomics in identifying malignancies. It highlights challenges such as interpretability, limited datasets, and operator dependence, and calls for standardization, prospective multicenter trials, and advances in explainable AI and personalized medicine. The authors emphasize that multidisciplinary collaboration and further algorithm refinement could enhance diagnostic accuracy and patient outcomes.
By Milad Yousefi, Hadi Vahedi, Shadi Farabi Maleki, Mahya Ahmadpour Youshanlui, Aida Jafari, Parisa Rostami, Kais I. Abdul-Lateef Al-Abdullah, Ryszard Tadeusiewicz, Pawel Plawiak, Roohallah Alizadehsani, Siamak Pedrammehr
arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.
By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)
The paper presents a thyroid nodule segmentation framework for ultrasound images that combines DeepLabV3+ with EfficientNet‑B5, channel‑attention modules (Squeeze‑and‑Excitation, CBAM, GCA), and a post‑hoc conformal prediction layer to provide pixel‑level uncertainty estimates. Experiments on the TN3K benchmark show that the CBAM‑augmented model with Dice‑BCE loss outperforms existing methods. The approach aims to improve the first stage of a CAD pipeline for malignancy classification and metastatic risk assessment.
By Francesco Teodori, Pierpaolo Brutti, Umberto Ferraro Petrillo
arXiv:2607. 02185v1 Announce Type: cross Abstract: Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability.
By Mohammad Amanour Rahman
Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability.