Missing-Modality Medical Image Segmentation

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10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Progressive multi-scale tumor-state memory for incomplete multimodal brain tumor segmentation
Oct 19, 2026Milad Rostamian, Mohammadreza Mohammadi, Mohsen SoryaniMissing-Modality Medical Image Segmentation Medical Image Segmentation
School of Computer Engineering, Iran University of Science and Technology (IUST), Hengam Street, Resalat Square, 13114-16846, Tehran, Iran
LASSNet: Level-Aware Availability-Conditioned Spatial-Semantic Fusion for Brain Tumor Segmentation with Missing MRI Modalities
Sep 6, 2026Haobin Chen, Ao Chang, Rundong Wang +3Missing-Modality Medical Image Segmentation Medical Image Segmentation
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China · Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China · College of Information Science and Technology, Donghua University
MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities
Aug 20, 2026Tarun Kumar Garg, Vaanathi SundaresanWeakly Supervised Image Segmentation Missing-Modality Medical Image Segmentation
Department of IISc Mathematics Initiative, Indian Institute of Science, Bengaluru 560012, Karnataka, India · Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru 560012, Karnataka, India
Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation
Jun 29, 2026Seunghun Baek, Jihwan Park, Jaeyoon Sim +3Representation Learning Multimodal Robustness
Pohang University of Science and Technology, Pohang, South Korea
LARGO: Low-Rank Hypernetwork for Handling Missing Modalities
May 7, 2026Niels Vyncke, Pooya Ashtari, Aleksandra PižuricaMissing-Modality Learning Cross-Modality Medical Image Segmentation
Department of Telecommunications and Information Processing Ghent University Ghent, Belgium
Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities
Apr 24, 2026Peibo Song, Xiaotian Xue, Jinshuo Zhang +5Cross-Modality Medical Image Segmentation Hybrid CNN-Transformer Architectures
School of Mathematics, Shandong University · 2GSFS, The University of Tokyo · 3MedVisAI Lab +2
CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities
Apr 15, 2026Bo Liu, Yulong Zou, Jin HongMissing-Modality Medical Image Segmentation Brain Tumor Segmentation
School of Information Engineering, Nanchang University, Nanchang 330031, China · School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
CLoE: Expert Consistency Learning for Robust Missing Modality Segmentation
Mar 10, 2026Xinyu Tong, Meihua Zhou, Bowu Fan +1Missing-Modality Medical Image Segmentation Medical Image Segmentation
University of Chinese Academy Sciences · School of Medicine, Southeast University · Zhejiang University
Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation
Mar 5, 2026SangHyuk Kim, Daniel Haehn, Sumientra RampersadMissing-Modality Medical Image Segmentation Medical Image Analysis
University of Massachusetts Boston
A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities
Apr 22, 2024Ming Kang, Fung Fung Ting, Shier Nee Saw +3Cross-Modal Knowledge Distillation Cross-Modality Medical Image Segmentation
School of Information Technology, Monash University, Malaysia Campus, Subang Jaya 47500, Malaysia · Augmented Intelligence and Multimodal analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia · Monash Medical AI Group, Monash University, Clayton VIC 3800, Australia +1

Oct 19, 2026J/K move · Enter open · S save

Progressive multi-scale tumor-state memory for incomplete multimodal brain tumor segmentation

Milad Rostamian, Mohammadreza Mohammadi, Mohsen Soryani

School of Computer Engineering, Iran University of Science and Technology (IUST), Hengam Street, Resalat Square, 13114-16846, Tehran, Iran

Missing-Modality Medical Image Segmentation Medical Image Segmentation Brain Tumor Segmentation Multimodal Fusion BraTS Multi-Scale Feature Fusion

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Brain tumor segmentation uses complementary magnetic resonance imaging (MRI) sequences, but some sequences may be unavailable in practice. Their absence changes the information available for segmenting different tumor regions. Fusion methods can support different input subsets. However, fusion at several scales does not by itself specify how tumor evidence is retained and updated to guide fusion at later stages. We propose a progressive multi-scale tumor-state memory network (PMTM) for segmentation from any non-empty subset of T1ce, T1, FLAIR, and T2. PMTM maintains a compact state for the current subject across the encoder stages. At each stage, we use assignment maps to pool spatial features into ordered evidence vectors for each available modality. We average these vectors across the available modalities, and a learned gate combines this evidence with the previous state. The up- dated state guides residual fusion at that stage and is used in the state update at the next stage. A modality-token mixer also models interactions among the available modalities at each stage. We apply semantic supervision to the assignment maps at all four stages. We evaluate PMTM on BraTS2018, BraTS2020, and BraTS2023 GLI across all 15 non-empty modality subsets. Averaged over the three tumor regions and the 15 subsets, PMTM achieves Dice scores of 75.31%, 77.54%, and 80.47% on BraTS2018, BraTS2020, and BraTS2023 GLI, respectively. These scores are higher than those of RobustSeg, RFNet, and mmFormer in our experiments on each dataset. Component ablations and internal state analysis are performed on BraTS2020.