RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis
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Abstract (arXiv)
Rheumatoid arthritis (RA) assessment from hand radiographs requires multi-level analysis and modeling of anatomical structures and fine-grained local pathological changes. However, existing public resources do not support such unified multi-level analysis, often lacking full-hand coverage, fine-grained annotations, and consistent integration with clinical scoring systems. In particular, annotations that enable quantitative analysis of bone erosion (BE) remain scarce. RAM-H1200 contains 1,200 hand radiographs collected from six medical centers, with multi-level annotations including (i) whole-hand bone structure instance segmentation, (ii) pixel-level BE masks, (iii) SvdH-defined joint regions of interest, and (iv) joint-level SvdH scores for both BE and joint space narrowing (JSN). It is designed to evaluate whether models can jointly capture anatomical structure, localized erosive pathology, and clinically standardized RA severity from hand radiographs. The proposed BE masks enable, for the first time, quantitative BE analysis beyond coarse categorical grading by providing explicit spatial supervision for lesion extent and morphology. To our knowledge, RAM-H1200 is the first public large-scale benchmark that jointly supports whole-hand bone structure instance segmentation, pixel-level BE delineation, and clinically grounded joint-level SvdH scoring for both BE and JSN. Results across benchmark tasks show that anatomical modeling is substantially more mature than quantitative BE analysis: whole-hand bone segmentation achieves strong performance, whereas BE segmentation remains a major open challenge. By unifying anatomical structure modeling, quantitative lesion analysis, and clinically grounded SvdH scoring, RAM-H1200 provides a single benchmark for comprehensive RA analysis on hand radiographs.
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References · 24
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2023Segment Anythingcited by 8,234
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
- 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
- 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
- 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 642
- 2021LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inferencecited by 475
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- 2022EfficientFormer: Vision Transformers at MobileNet Speedcited by 241
- 2024MambaVision: A Hybrid Mamba-Transformer Vision Backbonecited by 224
- 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
- 2024MedMamba: Vision Mamba for Medical Image Classificationcited by 88
- 2021Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rayscited by 6
- 2025RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritiscited by 5
- 2022A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritiscited by 4
- 2023A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritiscited by 3
- 2023Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learningcited by 3
- 2024Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritiscited by 3
- 2024BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographscited by 3
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