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
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.
By Medhansh Sharma
The paper evaluates multimodal decision models, specifically CLEF and CLEF‑Flash, on 2,818 breast ultrasound images from three datasets (Dataset B, BUSI, BUS‑BRA). Using three different prompts—minimal labels, explicit classification, and sonographic criteria—the study reports accuracy, macro F1, balanced accuracy, precision, throughput, and latency. CLEF‑Flash with a clinical prompt achieves the highest accuracy across all datasets, while CLEF with a task‑guided prompt yields the best macro F1 and balanced accuracy on BUSI; however, all configurations underperform a majority‑class predictor on BUS‑BRA, highlighting frequent benign‑to‑malignant misclassifications.
By Tran Cao Minh, Hai Tran, Ha Minh Tan
The paper introduces SonoCorpus, an open dataset of 456,963 ultrasound images with 1,626,085 expert masks from 53 public sources across 24 clinical applications and 17 countries, and SonoBase, an interactive segmentation foundation model pretrained on this data. SonoBase outperforms existing models (SAM2, MedSAM2, MedSAM3) on fifteen diverse evaluation datasets, matching specialist models and achieving clinically relevant accuracy for metrics such as ejection fraction, fetal head circumference, and gestational age. The authors provide full reproducibility resources, including checkpoints, optimizer states, and starter code, to enable community adoption and further development.
By Chao Qin, Fahad Shahbaz Khan, Salman Khan, Sarim Ather, Siddiq Anwar, Rao Muhammad Anwer, Shadab Khan
Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely on pulmonary image content. This study evaluates...
arXiv:2608.30467v1 Announce Type: new
Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely...
By Abdullah Al Mamun, Md. Nasif Osman Khansur, Md Ashraful Hossen Akash, Md. Kishor Morol, Tze Hui Liew