arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.
By Pranav Kaliaperumal, Manisha Kaliaperumal
arXiv:2606. 30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation.
By Seunghun Baek, Jihwan Park, Jaeyoon Sim, Hoseok Lee, Seungjoo Lee, Won Hwa Kim
arXiv:2608. 19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing.
By Tarun Kumar Garg, Vaanathi Sundaresan
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:2608.23745v1 Announce Type: cross
Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and diseas...
By Mohammad Mahdi Danesh Pajouh, Sara Saeedi
The paper introduces a method for generalizable brain tumor segmentation in the BraTS 2026 Challenge. It builds on the nnU-Net framework with a large residual encoder, adding semi‑supervised learning via pseudo‑labels and a tumor‑aware deformable augmentation that locally deforms lesions while preserving surrounding anatomy. The approach improves Dice and NSD scores across all tumor regions compared to labeled‑only baselines, demonstrating the complementary benefits of self‑training and the proposed augmentation.
By Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca, Andre Gustavo Hochuli