--- license: cc-by-4.0 pretty_name: LMOD+ (Sample Subset) size_categories: - 1K πŸ“„ Paper: [ACM Transactions on Computing for Healthcare (2026)](https://doi.org/10.1145/3801746) Β· [arXiv:2509.25620](https://arxiv.org/abs/2509.25620) > 🌐 Project page: https://kfzyqin.github.io/lmod_plus/ ## Quick start ```python from datasets import load_dataset # Anatomical recognition with bounding boxes ds = load_dataset("Euanyu/LMOD-Plus-Sample", "origa", split="test") print(ds[0]["bboxes"]) # [{'region_type': 'Optic Disc', 'xmin': 0.528, 'ymin': 0.400, 'xmax': 0.651, 'ymax': 0.626}, ...] # Multi-class disease diagnosis ds = load_dataset("Euanyu/LMOD-Plus-Sample", "eye_disease", split="test") print(ds[0]["diagnosis"]) # 'cataract' # Disease staging + demographics ds = load_dataset("Euanyu/LMOD-Plus-Sample", "oimhs", split="test") print(ds[0]["mh_stage"], ds[0]["patient_age"], ds[0]["patient_sex"]) ``` Streaming works too, which is useful for the larger `origa` config: ```python ds = load_dataset("Euanyu/LMOD-Plus-Sample", "origa", split="test", streaming=True) ``` ## Configurations | Config | Instances | Modality | Primary task | Annotations | | --- | --- | --- | --- | --- | | `cataract_1k` | 200 | Surgical scenes (SS) | Anatomical recognition | 688 boxes over 15 region types | | `cau001` | 200 | Lens photographs (LP) | Anatomical recognition | 655 boxes (eye / iris / pupil) | | `oimhs` | 76 | OCT | Staging + demographics | 508 boxes, 206 masks, MH stage 1–4 | | `origa` | 200 | Color fundus (CFP) | Binary screening | 400 boxes (disc / cup), CDR | | `eye_disease` | 400 | Color fundus (CFP) | Multi-class diagnosis | 4-class label | | **Total** | **1,076** | | | | ## Schema All configs share a common core: | Field | Type | Description | | --- | --- | --- | | `image_id` | `string` | Identifier, unique within a config | | `dataset` | `string` | Source dataset name | | `modality` | `string` | `SS`, `LP`, `OCT`, or `CFP` | | `image_type` | `string` | Free-text modality description from the annotation pipeline | | `image` | `Image` | The clean, un-annotated image | | `relative_path` | `string` | Path to this image inside the raw archives (see below) | Config-specific fields: - **`cataract_1k`, `cau001`, `origa`, `oimhs`** β€” `bboxes`: list of `{region_type, xmin, ymin, xmax, ymax}` - **`origa`** β€” `glaucoma_label` (`Glaucoma` / `Non-Glaucoma`), `cdr` (cup-to-disc ratio), `ecc_cup`, `ecc_disc` - **`oimhs`** β€” `segmentations`: list of `{segmentation_class, mask}` (retina / choroid); `mh_stage` (1–4); `patient_id`, `patient_age`, `patient_sex`, `eye_side`; image-quality flags `low_signal_strength`, `signal_shield`, `image_blur` - **`eye_disease`** β€” `diagnosis`: `cataract`, `diabetic_retinopathy`, `glaucoma`, or `normal` ### Bounding box convention Coordinates are **normalized to `[0, 1]`** with the origin at the **top-left** corner. Convert to pixels by multiplying by the image width and height: ```python from PIL import ImageDraw row = ds[0] img = row["image"].convert("RGB") W, H = img.size draw = ImageDraw.Draw(img) for b in row["bboxes"]: draw.rectangle([b["xmin"] * W, b["ymin"] * H, b["xmax"] * W, b["ymax"] * H], outline="lime", width=4) ``` `eye_disease` is a pure classification config and has no `bboxes` column. ## Raw archives The parquet files contain the clean images plus structured annotations, which covers most use cases. If you need the original on-disk layout β€” including the pre-rendered annotation overlays used to produce the paper's figures β€” each sub-dataset is also shipped as a tarball under `raw/`: ``` raw/subset_Cataract-1K.tar.gz 600 files 312 MB raw/subset_CAU001.tar.gz 600 files 202 MB raw/subset_OIMHS.tar.gz 716 files 78 MB raw/subset_ORIGA.tar.gz 800 files 1403 MB raw/subset_eye_disease.tar.gz 400 files 73 MB ``` ```python from huggingface_hub import hf_hub_download import tarfile p = hf_hub_download("Euanyu/LMOD-Plus-Sample", "raw/subset_OIMHS.tar.gz", repo_type="dataset") tarfile.open(p).extractall("./lmod_raw") ``` Each sample folder holds the clean image, an `information.json` (or `KEYINFORMATION*.json`), and an `annotated/` or `metadata/` subtree with overlays. The `image_path` field inside every JSON is relative to the subset root, so `subset_OIMHS/` + `100_30/visualization.png` resolves directly. ## Tasks LMOD+ supports four task families. This subset exercises all of them: 1. **Anatomical recognition** β€” localize structures (cornea, pupil, iris, optic disc/cup, retina, choroid, surgical instruments) via bounding boxes and segmentation masks. 2. **Disease screening** β€” binary detection, e.g. glaucoma vs. non-glaucoma in `origa`. 3. **Disease staging** β€” severity grading, e.g. macular hole stages 1–4 in `oimhs`. 4. **Demographic prediction** β€” infer patient age and sex from the image (`oimhs`). Reported zero-shot performance in the paper is far from saturated: the strongest of the 24 evaluated MLLMs reaches roughly **58% accuracy** on disease screening. ## Images are unmodified Image bytes are embedded **bit-exact** from the source pipeline β€” no resizing, re-encoding, or recompression. Note that this means `origa` inherits the pipeline's PNG conversion of originally-JPEG fundus photographs, which is why that config is disproportionately large (~700 MB for 200 images at 2518Γ—2048). ## Source datasets and attribution LMOD+ is an aggregation of publicly released ophthalmic datasets, re-annotated with a unified multi-granular schema. Under CC BY 4.0 you must credit LMOD+; you are **also** responsible for honouring the terms of each upstream source, which are not superseded by this repository's license. | Sub-dataset | Modality | Upstream source | | --- | --- | --- | | Cataract-1K | Surgical scenes | Cataract-1K cataract surgery dataset | | CAU001 | Lens photographs | See the LMOD+ paper, Β§Datasets | | OIMHS | OCT | OIMHS β€” OCT images with macular hole segmentation | | ORIGA | Color fundus | ORIGA-light retinal fundus dataset | | Eye Diseases | Color fundus | 4-class fundus classification collection; see the LMOD+ paper | If you redistribute or build on this subset, cite both LMOD+ and the relevant upstream dataset. ## Demographics and privacy The `oimhs` config includes `patient_id`, `patient_age`, `patient_sex`, and `eye_side`. These are carried over from the public upstream release and are required for the demographic-prediction task; `patient_id` is a pseudonymous integer from the source dataset and enables patient-level grouping so that images from the same patient do not straddle a train/test boundary. No direct identifiers are present. All internal file paths from the annotation pipeline have been stripped from both the parquet files and the raw archives. ## Known issues - **Region-label typos.** Two boxes in `cataract_1k` carry malformed labels: `case5013_14` has a `pupil1` box alongside its `Pupil` box, and `case5014_18` has `cornea1` alongside `Cornea`. These are preserved as-is so the data matches what was evaluated in the paper. Filter or normalize them if your pipeline is label-sensitive. - **Partial anatomical coverage in `cau001`.** 50 of 200 samples annotate only the iris and pupil, without an enclosing `Left Eye` / `Right Eye` box. - **Class balance is by construction.** The per-class counts in this subset (100 per diagnosis, 19 per macular hole stage) were sampled to be balanced and do **not** reflect prevalence in the full benchmark or in a clinical population. ## Relationship to the full release This subset is drawn from the full LMOD+ benchmark (32,633 instances; additionally covering Harvard FairSeg, CatDet2, REFUGE, IDRiD, and G1020). It is intended for format inspection and prototyping β€” **do not report benchmark numbers on this subset**, as the sample sizes are far too small for meaningful comparison and the class balance is artificial. ## Citation ```bibtex @article{10.1145/3801746, author = {Qin, Zhenyue and Liu, Yang and Yin, Yu and Ding, Jinyu and Zhang, Haoran and Li, Anran and Campbell, Dylan and Wu, Xuansheng and Zou, Ke and Keenan, Tiarnal D. L. and Chew, Emily Y. and Lu, Zhiyong and Tham, Yih Chung and Liu, Ninghao and Zhang, Xiuzhen and Chen, Qingyu}, title = {LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology}, year = {2026}, issue_date = {July 2026}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {7}, number = {3}, url = {https://doi.org/10.1145/3801746}, doi = {10.1145/3801746}, journal = {ACM Trans. Comput. Healthcare}, month = jun, articleno = {52}, numpages = {38} } ``` The preliminary version of this benchmark appeared at NAACL 2025: ```bibtex @inproceedings{qin-etal-2025-lmod, title = {{LMOD}: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models}, author = {Qin, Zhenyue and Yin, Yu and Campbell, Dylan and Wu, Xuansheng and Zou, Ke and Liu, Ninghao and Tham, Yih Chung and Zhang, Xiuzhen and Chen, Qingyu}, booktitle = {Findings of the Association for Computational Linguistics: NAACL 2025}, month = apr, year = {2025}, address = {Albuquerque, New Mexico}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2025.findings-naacl.135/}, doi = {10.18653/v1/2025.findings-naacl.135}, pages = {2501--2522} } ```