yankon0408/GAA-DETR-Cd

Cervical Fungus Lesion Detection Gaze Dataset (MICCAI 2025 Spotlight)

This repository contains the CD subset of the first large-scale Medical Lesion Detection Gaze Dataset, as presented in our MICCAI 2025 Spotlight paper: "Query-Level Alignment for End-to-End Lesion Detection with Human Gaze".

1. Dataset Overview

This dataset provides a unique combination of high-resolution cervical microscopical images, expert radiologist eye-tracking (gaze) data, and gold-standard lesion annotations. It is designed to facilitate research in gaze-assisted medical AI and human-computer interaction in clinical diagnostics.

Directory Structure

• image/: cervical microscopical images.
• gaze_data/: Raw gaze coordinates and scaling factors.
• annotations/: Lesion bounding boxes in COCO format.
• code/: Demonstration scripts (e.g., demo.ipynb) for data processing.

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2. Data Specifications

Images (/image)

Cervical microscopical images

Gaze Data (/gaze_data)

The original eye-tracking data is provided in the following format: [[x_coordinate, y_coordinate], scaling_factor]

• x, y: Coordinates relative to the scaled/resized image.
• scaling_factor: The ratio used to resize the original image to the display size during the gaze collection process.

Annotations (/annotations)

Annotations follow the standard MS COCO detection dataset format.

• Bounding Box Format: [x1, y1, width, height]

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3. Getting Started

A demonstration notebook is provided in code/demo.ipynb to show how to process the data. It includes steps to: 1. Load the images and corresponding gaze data. 2. Convert raw gaze_data points into attention heatmaps.

4. Citation and Acknowledgments

Primary Reference

If you use this dataset or the associated code in your research, please cite our MICCAI 2025 paper:

@inproceedings{kong2025query,
  title={Query-Level Alignment for End-to-End Lesion Detection with Human Gaze},
  author={Kong, Yan and Peng, Zhixiang and Yin, Yuan and Li, Yonghao and Cai, Jiangdong and Wang, Sheng and Wang, Qian and Fang, Yuqi and Shan, Caifeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={496--506},
  year={2025},
  organization={Springer}
}

Data Sources

The image data in this subset is sourced from the ComparisonDetector public databases. Please ensure you also cite the following original works when using this dataset:

@article{liang2018comparison,
  title={Comparison-based convolutional neural networks for cervical cell/clumps detection in the limited data scenario},
  author={Liang, Yixiong and Tang, Zhihong and Yan, Meng and Chen, Jialin and Liu, Qing and Xiang, Yao},
  journal={arXiv preprint arXiv:1810.05952},
  year={2018}
}

Contact: email to kongyan@smail.nju.edu.cn or issue in https://github.com/YanKong0408/GAA-DETR.