# RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis

arXiv: https://arxiv.org/abs/2605.05616 · HTML: https://mlsanity.com/paper/2605.05616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 78 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 24 of 24)

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- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
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- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
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- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
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- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays](https://mlsanity.com/paper/2104.13915/) (2021)
- [RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis](https://mlsanity.com/paper/2507.05193/) (2025)
- [A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis](https://mlsanity.com/paper/2205.09315/) (2022)
- [A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis](https://mlsanity.com/paper/2304.13938/) (2023)
- [Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning](https://mlsanity.com/paper/2311.06834/) (2023)
- [Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritis](https://mlsanity.com/paper/2406.09980/) (2024)
- [BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs](https://mlsanity.com/paper/2409.07304/) (2024)

## 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.