2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification Article Swipe
Jingwei Zhang
,
Anh Tien Nguyen
,
Han Xi
,
Vincent Quoc‐Huy Trinh
,
Hong Qin
,
Dimitris Samaras
,
Mahdi S. Hosseini
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.00678
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.00678
Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling effective and efficient modeling of wide context in 1D sequences. However, extending Mamba to vision tasks, which inherently involve 2D structures, results in spatial discrepancies due to the limitations of 1D sequence processing. On the other hand, current 2D SSMs inherently model 2D structures but they suffer from prohibitively slow computation due to the lack of efficient parallel algorithms. In this work, we propose 2DMamba, a novel 2D selective SSM framework that incorporates the 2D spatial structure of images into Mamba, with a highly optimized hardware-aware operator, adopting both spatial continuity and computational efficiency. We validate the versatility of our approach on both WSIs and natural images. Extensive experiments on 10 public datasets for WSI classification and survival analysis show that 2DMamba improves up to 2.48% in AUC, 3.11% in F1 score, 2.47% in accuracy and 5.52% in C-index. Additionally, integrating our method with VMamba for natural imaging yields 0.5 to 0.7 improvements in mIoU on the ADE20k semantic segmentation dataset, and 0.2% accuracy improvement on ImageNet-1K classification dataset. Our code is available at https://github.com/AtlasAnalyticsLab/2DMamba.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.00678
- https://arxiv.org/pdf/2412.00678
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405033443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405033443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.00678Digital Object Identifier
- Title
-
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-01Full publication date if available
- Authors
-
Jingwei Zhang, Anh Tien Nguyen, Han Xi, Vincent Quoc‐Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. HosseiniList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.00678Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.00678Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2412.00678Direct OA link when available
- Concepts
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Image (mathematics), Representation (politics), Pixel, Artificial intelligence, Computer science, Computer vision, State (computer science), Space (punctuation), Pattern recognition (psychology), Algorithm, Political science, Operating system, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.context | 58 |
| abstract_inverted_index.current | 89 |
| abstract_inverted_index.images. | 158 |
| abstract_inverted_index.imaging | 199 |
| abstract_inverted_index.involve | 70 |
| abstract_inverted_index.natural | 157, 198 |
| abstract_inverted_index.propose | 115 |
| abstract_inverted_index.results | 73 |
| abstract_inverted_index.spatial | 75, 127, 141 |
| abstract_inverted_index.various | 8 |
| abstract_inverted_index.2DMamba, | 116 |
| abstract_inverted_index.C-index. | 190 |
| abstract_inverted_index.However, | 62 |
| abstract_inverted_index.accuracy | 186, 215 |
| abstract_inverted_index.adopting | 139 |
| abstract_inverted_index.analysis | 170 |
| abstract_inverted_index.approach | 152 |
| abstract_inverted_index.contexts | 4 |
| abstract_inverted_index.dataset, | 212 |
| abstract_inverted_index.dataset. | 220 |
| abstract_inverted_index.datasets | 164 |
| abstract_inverted_index.enabling | 51 |
| abstract_inverted_index.handling | 33 |
| abstract_inverted_index.improves | 174 |
| abstract_inverted_index.modeling | 1, 55 |
| abstract_inverted_index.parallel | 109 |
| abstract_inverted_index.semantic | 210 |
| abstract_inverted_index.sensing. | 18 |
| abstract_inverted_index.sequence | 83 |
| abstract_inverted_index.survival | 169 |
| abstract_inverted_index.validate | 147 |
| abstract_inverted_index.Extensive | 159 |
| abstract_inverted_index.Recently, | 36 |
| abstract_inverted_index.available | 224 |
| abstract_inverted_index.effective | 52 |
| abstract_inverted_index.efficient | 54, 108 |
| abstract_inverted_index.essential | 6 |
| abstract_inverted_index.extending | 63 |
| abstract_inverted_index.framework | 122 |
| abstract_inverted_index.including | 10 |
| abstract_inverted_index.operator, | 138 |
| abstract_inverted_index.optimized | 136 |
| abstract_inverted_index.quadratic | 30 |
| abstract_inverted_index.selective | 40, 120 |
| abstract_inverted_index.structure | 128 |
| abstract_inverted_index.Giga-Pixel | 11 |
| abstract_inverted_index.challenges | 26 |
| abstract_inverted_index.complexity | 31, 47 |
| abstract_inverted_index.continuity | 142 |
| abstract_inverted_index.inherently | 69, 92 |
| abstract_inverted_index.introduced | 38 |
| abstract_inverted_index.sequences. | 35, 61 |
| abstract_inverted_index.structures | 95 |
| abstract_inverted_index.Efficiently | 0 |
| abstract_inverted_index.ImageNet-1K | 218 |
| abstract_inverted_index.algorithms. | 110 |
| abstract_inverted_index.computation | 102 |
| abstract_inverted_index.efficiency. | 145 |
| abstract_inverted_index.experiments | 160 |
| abstract_inverted_index.improvement | 216 |
| abstract_inverted_index.integrating | 192 |
| abstract_inverted_index.limitations | 80 |
| abstract_inverted_index.parallelism | 23 |
| abstract_inverted_index.processing. | 84 |
| abstract_inverted_index.structures, | 72 |
| abstract_inverted_index.versatility | 149 |
| abstract_inverted_index.improvements | 204 |
| abstract_inverted_index.incorporates | 124 |
| abstract_inverted_index.parallelism, | 50 |
| abstract_inverted_index.segmentation | 211 |
| abstract_inverted_index.Additionally, | 191 |
| abstract_inverted_index.computational | 144 |
| abstract_inverted_index.discrepancies | 76 |
| abstract_inverted_index.prohibitively | 100 |
| abstract_inverted_index.classification | 167, 219 |
| abstract_inverted_index.hardware-aware | 137 |
| abstract_inverted_index.Transformer-based | 19 |
| abstract_inverted_index.https://github.com/AtlasAnalyticsLab/2DMamba. | 226 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |