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
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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.
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.
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.
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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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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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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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.