mlsanityData to 7 Oct 2026

CenterMamba-SAM: Center-Prioritized Scanning and Temporal Prototypes for Brain Lesion Segmentation

arXiv 2511.01243 ↗First posted 3 Nov 2025cs.CVcited by 8

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Abstract (arXiv)

Brain lesion segmentation remains challenging due to small, low-contrast lesions, anisotropic sampling, and cross-slice discontinuities. We propose CenterMamba-SAM, an end-to-end framework that freezes a pretrained backbone and trains only lightweight adapters for efficient fine-tuning. At its core is the CenterMamba encoder, which employs a novel 3x3 corner-axis-center short-sequence scanning strategy to enable center-prioritized, axis-reinforced, and diagonally compensated information aggregation. This design enhances sensitivity to weak boundaries and tiny foci while maintaining sparse yet effective feature representation. A memory-driven structural prompt generator maintains a prototype bank across neighboring slices, enabling automatic synthesis of reliable prompts without user interaction, thereby improving inter-slice coherence. The memory-augmented multi-scale decoder integrates memory attention modules at multiple levels, combining deep supervision with progressive refinement to restore fine details while preserving global consistency. Extensive experiments on public benchmarks demonstrate that CenterMamba-SAM achieves state-of-the-art performance in brain lesion segmentation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 3 Nov 2025.

Builds on

23 of 33 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2023Segment Anythingcited by 8,158
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  8. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  10. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  11. 2024VMamba: Visual State Space Modelcited by 955
  12. 2020Designing Network Design Spacescited by 954
11 more
  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  2. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
  3. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  4. 2023Segment Anything in Medical Imagescited by 226
  5. 2024EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mambacited by 146
  6. 2022ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation datasetcited by 104
  7. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  8. 2024RainMamba: Enhanced Locality Learning with State Space Models for Video Derainingcited by 56
  9. 2023The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challengecited by 25
  10. 2025SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognitioncited by 19
  11. 2025Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017cited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 7.

8 papers.

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