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OLDA-Bench

An Interpretable AI System for Oral Leukoplakia Progression: From Early Screening to Lesion Delineation

Overview

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.

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

Dataset Access

The dataset is available through the Hugging Face Dataset Hub:

https://huggingface.co/datasets/gy-chen/OLDA-Bench-Dataset

Access requests are reviewed manually and granted for academic and non-commercial research purposes only.

📦 Dataset Structure

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:

Leukoplakia
Leukoplakia Cancer

The .json annotation files provide pixel-level segmentation masks and category labels for each lesion instance.

📊 Dataset Split

Training set: 389 images
Validation set: 44 images

🧪 Benchmark with MMdetection3.x & Detection2

🔹 Training

# 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.

🔹 Evaluation

After training, evaluate a model checkpoint on the test set:

# 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

🔹Additional Info

The OLDA-Bench is based on our previous work (environment code base) published in IEEE JBHI CDTM and OLPR.

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