Introduces RAM-W600, the first public multi-task dataset of wrist conventional radiographs with instance segmentation annotations and Sharp/van der Heijde bone erosion scores for rheumatoid arthritis research.
Medmamba: Vision mamba for medical image classification
13 Pith papers cite this work. Polarity classification is still indexing.
13
Pith papers citing it
abstract
Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies while maintaining linear computational complexity. Inspired by it, we proposed MedMamba, the first Vision Mamba for generalized medical image classification. Concretely, we introduced a novel hybrid basic block named SS-Conv-SSM, which purely integrates the convolutional layers for extracting local features with the abilities of SSM to capture long-range dependencies, aiming to model medical images from different image modalities efficiently. By employing the grouped convolution strategy and channel-shuffle operation, MedMamba successfully provides fewer model parameters and a lower computational burden for efficient applications without sacrificing accuracy. We thoroughly evaluated MedMamba using 16 datasets containing ten imaging modalities and 411,007 images. Experimental results show that MedMamba demonstrates competitive performance on most tasks compared with the state-of-the-art methods. This work aims to explore the potential of Vision Mamba and establish a new baseline for medical image classification, thereby providing valuable insights for developing more powerful Mamba-based artificial intelligence algorithms and applications in medicine. The source codes and all pre-trained weights of MedMamba are available at https://github.com/YubiaoYue/MedMamba.
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RAM-H1200 introduces a public dataset of 1,200 hand X-rays with whole-hand bone segmentation, pixel-level bone erosion masks, and joint-level SvdH scores for both erosion and narrowing to enable unified RA analysis.
Anatomy-Slot decomposes patch tokens into emergent anatomical slots and aligns them across eyes via bidirectional cross-attention, reporting a 4.2 AUC gain over ViT-L on ODIR-5K.
HypoExplore uses LLMs for hypothesis-driven evolutionary search with a Trajectory Tree and Hypothesis Memory Bank to discover lightweight vision architectures, reaching 94.11% accuracy on CIFAR-10 from an 18.91% baseline and generalizing to other datasets including state-of-the-art on MedMNIST.
Mamba-based visual and tabular models with mixed fusion achieve competitive balanced accuracy and higher recall than prior methods on skin and oral cancer datasets while enabling SHAP interpretability.
The paper presents a case-aware multimodal knowledge graph approach for medical image classification that retrieves similar cases, propagates knowledge via graph attention, and refines predictions with reliability estimates.
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
Deep vision models predict health insurance type from normal chest X-rays at AUC ~0.70, indicating capture of socioeconomic signals beyond demographics.
A comprehensive review synthesizing AI-driven sperm analysis across computer vision tasks, multimodal fusion, and a staged clinical translation roadmap.
Introduces the first MRI-based PAS dataset and 3DSAMba, a 3D SAM with adapter, MLAM, and FSSM modules, claiming improved lesion segmentation and PAS diagnosis via released code and data.
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
Hybrid EfficientNetV2-M and Vision Mamba architecture achieves strong binary classification performance on abnormality-centered mammography ROIs from CBIS-DDSM.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis
Introduces RAM-W600, the first public multi-task dataset of wrist conventional radiographs with instance segmentation annotations and Sharp/van der Heijde bone erosion scores for rheumatoid arthritis research.
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RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis
RAM-H1200 introduces a public dataset of 1,200 hand X-rays with whole-hand bone segmentation, pixel-level bone erosion masks, and joint-level SvdH scores for both erosion and narrowing to enable unified RA analysis.
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Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis
Anatomy-Slot decomposes patch tokens into emergent anatomical slots and aligns them across eyes via bidirectional cross-attention, reporting a 4.2 AUC gain over ViT-L on ODIR-5K.
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Agentic Discovery with Active Hypothesis Exploration for Visual Recognition
HypoExplore uses LLMs for hypothesis-driven evolutionary search with a Trajectory Tree and Hypothesis Memory Bank to discover lightweight vision architectures, reaching 94.11% accuracy on CIFAR-10 from an 18.91% baseline and generalizing to other datasets including state-of-the-art on MedMNIST.
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An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion
Mamba-based visual and tabular models with mixed fusion achieve competitive balanced accuracy and higher recall than prior methods on skin and oral cancer datasets while enabling SHAP interpretability.
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Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
The paper presents a case-aware multimodal knowledge graph approach for medical image classification that retrieves similar cases, propagates knowledge via graph attention, and refines predictions with reliability estimates.
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Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
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Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Deep vision models predict health insurance type from normal chest X-rays at AUC ~0.70, indicating capture of socioeconomic signals beyond demographics.
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Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation
A comprehensive review synthesizing AI-driven sperm analysis across computer vision tasks, multimodal fusion, and a staged clinical translation roadmap.
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3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum
Introduces the first MRI-based PAS dataset and 3DSAMba, a 3D SAM with adapter, MLAM, and FSSM modules, claiming improved lesion segmentation and PAS diagnosis via released code and data.
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Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
Benchmarks Vision Mamba variants for AI-generated image detection against CNN, ViT, and VLM detectors on diverse datasets and synthetic sources, reporting promise alongside limitations.
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A Hybrid Architecture for Benign-Malignant Classification of Mammography ROIs
Hybrid EfficientNetV2-M and Vision Mamba architecture achieves strong binary classification performance on abnormality-centered mammography ROIs from CBIS-DDSM.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.