Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies
<|reference_start|>Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies: The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in AI progress. This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral data for AI training with a focus on the pathology task of mitosis detection. One major challenge in using eye-gaze data is the low signal-to-noise ratio, which hinders the extraction of meaningful information. We tackled this by levering the properties of inter-observer eye-gaze consistencies and creating eye-gaze labels from consistent eye-fixations shared by a group of observers. Our study involved 14 non-medical participants, from whom we collected eye-gaze data and generated eye-gaze labels based on varying group sizes. We assessed the efficacy of such eye-gaze labels by training Convolutional Neural Networks (CNNs) and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline. Results indicated that CNNs trained with our eye-gaze labels closely followed the performance of ground-truth-based CNNs, and significantly outperformed the baseline. Although primarily focused on mitosis, we envision that insights from this study can be generalized to other medical imaging tasks.<|reference_end|>
@article{gu2024supporting,
    title={Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze
  Consistencies},
    author={Hongyan Gu, Zihan Yan, Ayesha Alvi, Brandon Day, Chunxu Yang, Zida Wu,
  Shino Magaki, Mohammad Haeri, Xiang 'Anthony' Chen},
    journal={arXiv preprint arXiv:2404.01656},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01656},
    primaryClass={cs.CV}
}