This study evaluates the classification accuracy of six modern deep‑learning architectures—VGG19, ResNet50, GoogleNet, ConvNeXt, EfficientNet, and Vision Transformers—on breast ultrasound images categorized by BI‑RADS. Using 2,945 training images and 936 validation images from 1,540 patients, the models were tested in full fine‑tuning, linear evaluation, and training‑from‑scratch settings. The best performance was achieved with full fine‑tuning, yielding 76.39 % accuracy and a 67.94 % F1 score.
By Malitha Gunawardhana, Norbert Zolek
The paper introduces the FAPT-M Dataset, a pathology‑confirmed multimodal collection of 910 breast patients that includes ultrasound images, structured clinical attributes, and diagnostic descriptions. It proposes a clinically guided multimodal framework that fuses DenseNet visual encoding, CLIP‑inspired text encoding, and lightweight clinical encoding, enhanced by clinical‑conditioned adaptive modulation, cross‑modal Transformer fusion, and dual‑path representation learning. The method achieves 77.64% accuracy, 73.38% F1‑score, and 89.74% AUC, outperforming CNN, Transformer, and vision‑language baselines, and demonstrates the value of three‑modality fusion through ablation and class‑balanced studies.
By Chuxi Nan, Di Wu, Hongming Guo, Ning Cao, Xiaohui Zhu, Zhaoting Shi, Jiawei Li
arXiv:2511. 15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts across imaging systems, acquisition protocols, and patient populations.
By Jingru Zhang, Saed Moradi, Ashirbani Saha
Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{screening}, \textit{diagnosis} and \textit{treatment planning}, where each stage involves distinct imaging modalities, task objectives, and reasoning patterns.
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
OmniMed-Jev is a new medical multimodal model that represents each decision as a Choice, Noul, or Score over a runtime-supplied candidate set, returning a full probability distribution for each decision. By unifying diverse imaging modalities and prediction tasks into a single candidate-conditioned probability model, it makes heterogeneous outputs comparable probabilities rather than task-specific strings. In controlled comparisons against a generative baseline, OmniMed-Jev’s reported probabilities align more closely with observed correctness, reducing calibration error by up to an order of magnitude and reliability error by up to two, while maintaining comparable point-prediction performance.
By Luyao Tang, Cheng Chen
arXiv:2607. 25933v1 Announce Type: cross Abstract: Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning.
By Rui Yang, Weihao Xuan, Yi Lin, Zhuhan Bao, Jonathan Chong Kai Liew, Matthew Yu Heng Wong, Nicol\'as Lescano, Nikita R. Paripati, Emily Ling-Lin Pai, Jiarui Liu, Heli Qi, Heng-Jui Chang, Benny Kai Guo Loo, Huitao Li, Kunyu Yu, Yufan Wang, Chuan Hong, Shijian Lu, Douglas Teodoro, Naoto Yokoya, Ross Koppel, Mona Diab, Hua Xu, David W. Bates, Nan Liu, Yifan Peng
arXiv:2606. 28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging.
By Maria Xenochristou, Ashutosh Joshi, Korosh Vatanparvar, Mohammad Abuzar Hashemi, Prasad Kasu, Deepak Bansal, Anchal Nema, Nivedita Wadhwa, Prashams S Jain, Rebecca Abraham, Will Kimbrough, Dilek Hakkani-Tur, Wilko Schulz-Mahlendorf
Background: Microvascular invasion (MVI) predicts recurrence and survival in hepatocellular carcinoma (HCC) but requires postoperative histopathology for diagnosis. We developed and validated a model...
The paper presents a method for adapting a single vision‑language model, Qwen3.5‑9B, to medical image parsing tasks by using task‑specific low‑rank adapters (LoRA) selected from the input question. The system employs a frozen 4‑bit backbone and customizes adapter ranks, prompts, and class rebalancing across six task families and seven imaging modalities, achieving strong performance on the FLARE 2026 Task 3 dataset and a hidden‑validation set. The authors provide code and demonstrate that task‑specific adapter configurations and image budgets improve performance across diverse medical imaging tasks.
By Taha Sabir, Alberto Montero Molina, Thinh Quang Ly, Veronika Dmitrenko, Mohammad Yaqub, Tausifa Jan Saleem
arXiv:2608. 03079v1 Announce Type: cross Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions.
By Ting Yin, Danning Li, Chen Shu, Xiaoxia Yao, Boyu Fu, Yujing Chang, Tianyu Shi, Mengna Feng, Jie Chen, Jing Fu, Xiuli Xiao, Tianlin Li, Mumin Shao, Jiaxin Bi, Wenchuan Zhang, Xiaoyan Wu, Xiao Han, Zhang Zhang, Yuhao Yi, Hong Bu
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