The study introduces a multitask conditional generative adversarial network (MT‑cGAN) that simultaneously synthesizes high‑resolution DESS‑like images and segments knee cartilage and menisci directly from quantitative MRI echo images. Evaluated on 508 knee MRIs from 361 subjects, MT‑cGAN achieved a mean Dice score of 0.84 for segmentation and the lowest coefficient of variation for T1ρ (1.84%) and T2 (1.81%) quantification, outperforming existing conditional GAN approaches. By eliminating the need for separate high‑resolution morphological scans, the method shortens scan times and supports clinical adoption of quantitative MRI.
arXiv:2609.31461v1 Announce Type: new Abstract: Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a k...
arXiv:2607. 20028v1 Announce Type: cross Abstract: Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging.
arXiv:2606. 07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire.
This study examined whether augmenting brain tumor MRI datasets with class‑specific DCGAN‑generated images improves classification performance. Using 7,200 scans across four tumor categories, a Swin Transformer classifier trained on real images alone achieved 96% accuracy, identical to the model trained with 500 synthetic images per class. Metrics such as macro F1 and ROC‑AUC showed no improvement, and FID scores indicated substantial distributional differences between real and synthetic images.
UnCapsTSR is an unsupervised transformer-based GAN framework designed to enhance the spatial resolution of low‑resolution wireless capsule endoscopy (WCE) images. It eliminates the need for explicit degradation modeling or paired LR‑HR data by using a Bilateral Total Variation loss to preserve spatial continuity. The authors introduce a new Kvasir Capsule dataset for training, validate generalizability on KID and GIANA datasets, and propose the Endoscopy Quality Metric (EndoQM) as a non‑reference evaluation tool, reporting 40–80% improvement in EndoQM over state‑of‑the‑art unsupervised methods.
The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.
arXiv:2609.39083v1 Announce Type: new Abstract: Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve d...
arXiv:2610.08482v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited,...
arXiv:2607. 18882v1 Announce Type: cross Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.
arXiv:2604. 23435v2 Announce Type: replace-cross Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers.
arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...
arXiv:2608.28773v1 Announce Type: cross Abstract: Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity sim...