WebMRIQC is a browser-based, open‑source platform that wraps the MRIQC engine to provide automated MRI image quality assessment without local installation. It handles DICOM‑to‑BIDS conversion, runs the containerized MRIQC pipeline on a shared compute node, and presents results in an interactive dashboard that benchmarks each scan against normative data. Validation on BraTS‑Africa and BraTS 2021 datasets shows strong agreement across thirteen image‑quality metrics, indicating that the web implementation can match native MRIQC performance.
By Philip Nkwam, Ifeoluwa Oladeji, Sekinat Zurakat-Aderibigbe, Jasmine Cakmak, Harrison Aduluwa, Confidence Raymond, Cliff Mokua, Abdulrazaq Zubair, Daniel Champanda, Tolulope Olusuyi, Maruf Adewole, Udunna Anazodo
arXiv:2608. 07541v1 Announce Type: cross Abstract: Transforming raw neuroimage archives into analysis-ready derivatives relies on three brittle stages: data standardization, modality-specific preprocessing, and quality control (QC).
By Yiyao Chen, Yucheng Li, Jungong Tong, Shaoqi Wang, Kunhao Zhou, Ziquan Wei, Monica Murea, Marissa DiPiero, Tingting Dan, Guorong Wu
arXiv:2608. 08896v1 Announce Type: new Abstract: Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to specialized biomedical engineering support.
By Bernes Lorier Atabonfack, Zion Kongbi Nfo, Ahmed Tahiru Issah, Tolulope Olusuyi, Clemence Ingabire, Mohammed Hardi Abdul Baaki, Mawuli Deku, Abdulrazaq Zubair, Alyasaa Anas, Raymond Confidence, Maruf Adewole, Udunna C. Anazodo
VGG16-MCA UNet is a hybrid neural network that combines an ImageNet‑pretrained VGG16 encoder with a decoder enhanced by a Multi‑Channel Attention module, trained using Focal Tversky loss to address class imbalance. The model was evaluated as a 2‑D, FLAIR‑only whole‑tumor segmenter on BraTS 2020 and LGG datasets, achieving a pixel‑level Dice of 95.10 % on BraTS and 88.32 % on LGG in a 5‑fold cross‑validation setting. Inference time is 66.32 ms per 256×256 slice on a single RTX 2060, only slightly slower than a VGG16‑UNet without attention.
whyItMatters":"The study provides a reproducible 2‑D FLAIR baseline for whole‑tumor segmentation, demonstrating high Dice scores and detailed reporting of training and evaluation protocols."
By Shubham Gajjar, Deep Joshi, Avi Poptani, Vishal Barot
arXiv:2607. 18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities.
By Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino
arXiv:2512. 03054v2 Announce Type: replace-cross Abstract: Federated Learning (FL) holds the potential to advance equality in health by enabling diverse institutions to collaboratively train deep learning (DL) models, even with limited data.
By Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien, Mathias Krohmer Zabaleta, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Maria Francesca Spadea
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...
By Changsheng Fang, Dayang Wang, T. Campbell Arnold, Enhao Gong, Srivathsa Pasumarthi
arXiv:2607. 16888v1 Announce Type: cross Abstract: Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service.
By Ranat Das Prangon, Istiaque Ahmed, Shajid Hasan Naim, Waseem Mustak Zisan, Hossain Md Shakhawat
arXiv:2609.21412v1 Announce Type: new
Abstract: Medical image segmenters often get worse when sites, scanner vendors, or protocols change. Continual test-time adaptation (CTTA) addresses this problem...
By Ruijie Huang
arXiv:2606. 26947v1 Announce Type: cross Abstract: While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG) remains a challenging task.
By Wenwang Huang, Yusen Fu, Junjie Wang, Mengfei Huang, Yulin Li, Gan Liu, Jing Cai, Yancheng He, Zhuotao Tian
arXiv:2608. 10291v1 Announce Type: cross Abstract: Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructure.
By Lisa K. Fischer, Mykhailo Riabets, Daniel Rueckert, Benedikt Wiestler, Anke Meyer-Baese, Sandeep Nagar
The paper proposes a two‑stage framework to improve images from low‑cost handheld ultrasound devices by mapping them to high‑quality hospital images. First, a cycle‑consistent style‑transfer model generates pixel‑aligned low‑quality/high‑quality pairs from unaligned real scans. Second, a Dual Degradation‑Guided Low‑Rank Adaptation (DDG‑LoRA) model fine‑tunes an LQ‑to‑HQ enhancement network, achieving a 16.7% FID improvement on the USenhance2023 dataset.