arXiv:2608. 28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) objectives to address heterogeneous anatomy and acquisition.
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.
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.
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.
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.
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...
arXiv:2608. 14705v1 Announce Type: cross Abstract: Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical imaging.
The paper introduces an uncertainty‑driven training framework for 3D CT lung nodule classification that uses validation‑based uncertainty estimates to reweight the loss, aiming to improve predictive performance and probability calibration. Two uncertainty quantification methods—Monte Carlo Dropout and Evidential Deep Learning—are evaluated across multiple backbone architectures (ResNet, DenseNet, EfficientNet, ViT, Swin) on the LIDC‑IDRI and NoduleMNIST3D datasets. The approach yields comparable classification accuracy to conventional training while substantially reducing expected calibration error, especially on convolutional backbones, and shows that simple temperature scaling can also achieve strong calibration.
Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning, masked reconstructi...
The study evaluates a deep‑learning model for pediatric pneumonia detection across chest X‑ray datasets from three countries, assessing discrimination, calibration, operating‑point transport, shortcut signals, and limited‑label recovery. Using a frozen DenseNet121 ensemble trained on Guangzhou data, the model achieved high internal AUROC (0.976) but performance dropped when applied zero‑shot to Bangladesh (AUROC 0.798) and Vietnam (AUROC 0.742). Limited‑label adaptation with Platt recalibration restored sensitivity but introduced significant specificity variability, highlighting the need to evaluate multiple performance dimensions in cross‑dataset transport studies.
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
arXiv:2605. 02942v2 Announce Type: replace Abstract: Fairness studies of medical imaging AI often explain subgroup performance gaps through under-representation in the training data.