arXiv Computer Vision

BrainIAC: Interactive 3D Brain Lesion Segmentation across Heterogeneous MRI Modalities with Online Adaptation

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.

arXiv Computer Vision

Aug 27

Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation

The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.

By Yuxiang Luo, Qing Xu, Hai Huang, Yuqi Ouyang, Xiangjian He, Zhen Chen, Wenting Duan, Jiebo Luo

agents computer-vision multimodal benchmarks

arXiv AI

Jul 21

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis

arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.

By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander

computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 16

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis

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.

By Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik, Furen Xiao, Vasudevan Janarthanan, Enkhzaya Chuluunbaatar, Byeong Kil Lee, Jeeho Ryoo

diffusion computer-vision benchmarks

arXiv Computer Vision

3d ago

How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRI

arXiv:2609.31573v1 Announce Type: new Abstract: Biomedical image segmentation is central to medical image analysis, but practical deployment often faces limited annotations, memory constraints, and c...

By Ziyao Shang, Pouya Sadeghi, Letian Jiang, Alexander Wong, Sirisha Rambhatla

computer-vision

arXiv Computer Vision

Aug 26

Multi-Stage Prompt-Guided Feature Modulation for Generalizable Brain Tumor Segmentation

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

computer-vision

arXiv AI

Jun 12

BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning

arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.

By Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang

computer-vision fine-tuning

arXiv AI

Sep 15

Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...

By Paul-Gabriel Nicolae, Irina Georgiana Mocanu

llms computer-vision multimodal

arXiv AI

Aug 21

MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation

arXiv:2510. 17529v3 Announce Type: replace-cross Abstract: Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up.

By Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ourselin, Michela Antonelli

llms computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 18

Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks

arXiv:2606. 18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization.

By Muge Zhang, Muhammad Ali Khaliq, Jamal Alsakran, Byeong Kil Lee, Jeeho Ryoo

diffusion computer-vision safety

arXiv Computer Vision

Sep 10

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...

By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot

computer-vision multimodal

arXiv Computer Vision

Aug 21

4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

arXiv:2604. 22700v2 Announce Type: replace Abstract: Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning.

By Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang

diffusion computer-vision benchmarks safety

arXiv AI

Jun 19

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.

By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms rag diffusion nlp benchmarks safety

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