Explainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM Visualization

Classificação de Tumores Cerebrais em Imagens de Ressonância Magnética Utilizando Técnicas de Aprendizado de Máquina Brain Tumor Classification in Magnetic Resonance Imaging Using Machine Learning Techniques

Multi-scale parallel perception and adaptive feature fusion for chest x-ray multi-disease detection

Objective. Chest x-ray (CXR) imaging is widely used for screening thoracic diseases. However, accurate multi-disease detection remains challenging because lesion sizes vary substantially, multiple abnormalities often coexist, and small or low-contrast lesions can be obscured by anatomical structures. This study aims to...

Biomedical Imaging CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

Few-Shot Learning of Lung Tumor Grading from Intraoperative OCT Imaging

We present an intelligent optical coherence tomography system integrated with few-shot learning for intraoperative lung tumor grading. The system enables real-time classification of normal, benign, and invasive adenocarcinoma tissues using limited labeled data with promising accuracy.

Correction: Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients

[This corrects the article DOI: 10.3389/fonc.2026.1687859.].

Comment: Prediction of Survival in Surgically Treated Glioblastoma Multiforme Utilizing DTI and Contrast-Enhanced MRI

The authors are commend the authors for addressing the clinically important question of preoperative imaging biomarkers for survival prediction in glioblastoma (GBM), however, several methodological, data integrity concerns are raised.