arXiv:2607. 15396v1 Announce Type: cross Abstract: Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used.
arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learn...
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...
arXiv:2607. 28858v1 Announce Type: cross Abstract: Automatic brain tumor segmentation from magnetic resonance imaging (MRI) has become a fundamental task in computer-assisted diagnosis, treatment planning, and disease monitoring.
arXiv:2512. 14937v2 Announce Type: replace-cross Abstract: Gliomas are the most common malignant brain tumors in adults and are among the most lethal.
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."
arXiv:2607. 19137v1 Announce Type: cross Abstract: Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy.
The paper introduces a lightweight Vision Transformer‑based U‑Net for brain tumor segmentation from MRI, combining U‑Net’s hierarchical feature extraction with a compact ViT bottleneck to capture both local and global context. With only 2.6 million trainable parameters, the model achieves a mean Intersection over Union of 0.8100 and a Dice score of 0.8446 on the TCGA LGG dataset, surpassing the baseline U‑Net by 3.75% and 3.15% respectively. Extensive quantitative and qualitative analyses, including confusion matrices, precision‑recall curves, and tumor size dependency studies, demonstrate the method’s effectiveness and robustness.
arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privac...
BrainIAC is a unified framework for 3D brain lesion segmentation that handles heterogeneous MRI modalities and adapts online during interactive segmentation. It combines a multi‑modal backbone trained with zero‑filling and random modality dropping, 3D interactive prompts that default to fully automatic predictions, and a two‑stage online adaptation guided by pseudo‑labels and a Click‑Centered Gaussian loss. Experiments on seven MRI datasets show that the components work synergistically, outperforming existing methods and generalizing to unseen modalities and pathologies.
Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy. Optimizing only paired pixel fidelity can suppress uncertain lesion enhancement, whereas adversarial or stochastic generative objectives can favor realistic post-contrast appearance without guaranteeing patient-specific lesion fidelity.
arXiv:2510.14383v4 Announce Type: replace Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...