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:2606. 18063v1 Announce Type: cross Abstract: Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity.
By Ruman Wang, Hangting Ye
The paper presents a lightweight CNN‑integrated Compact Convolutional Transformer (CCT) designed for multi‑scale feature learning in breast cancer mammography. With only 250,435 parameters, the model achieved 99‑100% accuracy across three datasets using 5‑fold cross‑validation, demonstrating robust generalization. Explainable AI components were added to clarify the classification process, aiming to increase clinical trust in resource‑constrained settings.
By Md Taimur Ahad (Department of Management North South University, Dhaka, Bangladesh), Ainuddin Ahmed (Department of Management North South University, Dhaka, Bangladesh)
arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.
By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat
The study evaluates lesion‑guided region‑of‑interest (ROI) deep learning for ovarian ultrasound classification, comparing it to global image, lesion contour, and contour‑based radiomics approaches across two public datasets. Using four deep‑learning architectures, the lesion‑guided ROI strategy achieved the highest accuracy (93.10% on MMOTU and 97.56% on OUD) with an AUC of 0.99, while requiring less annotation effort than contour‑based methods.
By Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi, Gernot Kronreif, Sepideh Hatamikia
arXiv:2511. 20956v2 Announce Type: replace-cross Abstract: Breast ultrasound (BUS) reporting relies on clinically meaningful lesion descriptors, including BI-RADS category, lesion shape, margin, echogenicity, posterior features, pathology, and histology.
By Rawa Mohammed, Mina Attin, Laxmi Gewali, Bryar Shareef