| --- |
| license: apache-2.0 |
| tags: |
| - medical-image-analysis |
| - oral-leukoplakia |
| - instance-segmentation |
| - deep-learning |
| - pytorch |
| --- |
| |
| # OLDA-Bench |
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| **An Interpretable AI System for Oral Leukoplakia Progression: From Early Screening to Lesion Delineation** |
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| --- |
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| ## Overview |
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| Oral leukoplakia is one of the most common oral potentially malignant disorders (OPMDs) and represents a critical precursor to oral cancer. However, current diagnostic workflows largely rely on invasive biopsy procedures and subjective clinical assessment, limiting their suitability for large-scale screening and longitudinal monitoring. |
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| To address these challenges, we introduce **OLDA-Bench (Oral Leukoplakia Diagnosis and Analysis Benchmark)**, the first benchmark specifically designed for oral leukoplakia progression recognition and interpretable lesion assessment |
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| ### Dataset Access |
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| The dataset is available through the Hugging Face Dataset Hub: |
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| https://huggingface.co/datasets/gy-chen/OLDA-Bench-Dataset |
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| Access requests are reviewed manually and granted for academic and non-commercial research purposes only. |
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| --- |
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| ## 📦 Dataset Structure |
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| Images identified as Leukoplakia or Leukoplakia with Cancer in the first stage are passed to the segmentation network. This stage uses a dataset in the standard COCO format, structured as follows: |
| ``` |
| ./root_data/ |
| │ |
| ├── train/ |
| │ ├── xxx.jpg |
| │ ├── ... |
| ├── val/ |
| │ ├── xxx.jpeg |
| │ ├── ... |
| ├── annotations |
| │ ├── train.json |
| │ ├── val.json |
| ``` |
| The instance segmentation task includes two categories: |
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| - **Leukoplakia** |
| - **Leukoplakia Cancer** |
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| The .json annotation files provide pixel-level segmentation masks and category labels for each lesion instance. |
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| --- |
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| ## 📊 Dataset Split |
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| - **Training set**: 389 images |
| - **Validation set**: 44 images |
| --- |
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| ## 🧪 Benchmark with MMdetection3.x & Detection2 |
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| ### 🔹 Training |
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| ```bash |
| # Single-GPU training |
| python tools/train.py\ |
| work_dirs_mask2former_swin_b/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco.py |
| |
| # Multi-GPU training |
| CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 ./tools/train.py\ |
| work_dirs_mask2former_swin_b/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco.py\ |
| --launcher pytorch |
| |
| ``` |
| You can replace config with any supported architecture name from mmdetection3.x. |
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| ### 🔹 Evaluation |
| After training, evaluate a model checkpoint on the test set: |
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| ```bash |
| # Single-GPU evaluation |
| python tools/test.py\ |
| work_dirs_sparseinsts/sparseinst_r50_iam_8xb8-ms-270k_coco.py\ |
| work_dirs_sparseinsts/best_coco_segm_mAP_iter_8500.pth |
| |
| # Multi-GPU evaluation |
| CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 tools/test.py \ |
| work_dirs_sparseinsts/sparseinst_r50_iam_8xb8-ms-270k_coco.py \ |
| work_dirs_sparseinsts/best_coco_segm_mAP_iter_8500.pth \ |
| --launcher pytorch |
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| ``` |
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| ### 🔹Additional Info |
| The OLDA-Bench is based on our previous work (environment code base) published in [*IEEE JBHI CDTM*](https://github.com/qklee-lz/CDTM) and [*OLPR*](https://github.com/qklee-lz/OLPR/). |
