arXiv Computer Vision

On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation

arXiv AI

Jun 12

Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

arXiv:2512. 14937v2 Announce Type: replace-cross Abstract: Gliomas are the most common malignant brain tumors in adults and are among the most lethal.

By Abhijeet Parida, Daniel Capell\'an-Mart\'in, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, Mar\'ia J. Ledesma-Carbayo, Marius George Linguraru

computer-vision

arXiv Computer Vision

Aug 25

Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images

arXiv:2605.05522v3 Announce Type: replace-cross Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...

By Aneesh Rangnekar, Joao Miranda, Natally Horvat, Stephanie Chahwan, Samir Alrayess, Aditya Apte, Aditi Iyer, Eve LoCastro, Revathi Ravella, Marc J Gollub, Iva Petkovska, Jesse Joshua Smith, Paul Romesser, Julio Garcia-Aguilar, Harini Veeraraghavan, Joseph O Deasy

llms computer-vision fine-tuning benchmarks safety

arXiv AI

Jul 20

Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation

arXiv:2607. 15396v1 Announce Type: cross Abstract: Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used.

By Agamdeep Chopra, Mehmet Kurt

computer-vision

arXiv Computer Vision

1d ago

Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

arXiv:2609.02600v1 Announce Type: new Abstract: This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentatio...

By Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca, Andre Gustavo Hochuli

computer-vision

arXiv Computer Vision

Aug 26

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023

arXiv:2506.12006v4 Announce Type: replace-cross Abstract: The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medi...

By Navodini Wijethilake, Reuben Dorent, Marina Ivory, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Mohamed Okasha, Anna Oviedova, Hexin Dong, Bogyeong Kang, Guillaume Sall\'e, Luyi Han, Ziyuan Zhao, Han Liu, Yubo Fan, Tao Yang, Shahad Hardan, Hussain Alasmawi, Santosh Sanjeev, Yuzhou Zhuang, Satoshi Kondo, Maria Baldeon Calisto, Shaikh Muhammad Uzair Noman, Cancan Chen, Ipek Oguz, Rongguo Zhang, Mina Rezaei, Susana K. Lai-Yuen, Satoshi Kasai, Yunzhi Huang, Chih-Cheng Hung, Mohammad Yaqub, Lisheng Wang, Benoit M. Dawant, Cuntai Guan, Ritse Mann, Vincent Jaouen, Tae-Eui Kam, Li Zhang, Jonathan Shapey, Tom Vercauteren

computer-vision fine-tuning multimodal benchmarks

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 Machine Learning

Jul 22

Cross-Dataset Generalization in Breast MRI Tumor Classification via Class-Wise Dataset Mixing

arXiv:2607. 18678v1 Announce Type: cross Abstract: Breast MRI is highly sensitive for detecting breast tumors, but exams contain many slices and require substantial reading time.

By Mohammad Ali Dadrast, Hamid Usefi

safety

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 29

Automated brain tumor detection in MRI images using CNN and ResNet architectures

arXiv:2606. 27405v1 Announce Type: cross Abstract: Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans.

By Annapurna V K, Asha N, K Paramesha, Shabana Sultana, Kirankumar Humse

fine-tuning

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 Machine Learning

Jun 10

Multimodal Brain Tumour Classification Using Feature Fusion

arXiv:2606. 11107v1 Announce Type: cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement.

By Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan, Volker Steuber

multimodal

arXiv AI

Aug 3

A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging

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.

By Diego J. Torrej\'on, Luna Y. Hern\'andez, Javier S\'anchez

llms computer-vision benchmarks

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