# Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies

arXiv: https://arxiv.org/abs/2404.01656 · HTML: https://mlsanity.com/paper/2404.01656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development](https://mlsanity.com/paper/2009.07386/) (2020)
- [Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis](https://mlsanity.com/paper/2204.02976/) (2022)
- [Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning](https://mlsanity.com/paper/2205.12466/) (2022)
- [Probabilistic Integration of Object Level Annotations in Chest X-ray Classification](https://mlsanity.com/paper/2210.06980/) (2022)
- [Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology](https://mlsanity.com/paper/2006.12683/) (2020)
- [Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge](https://mlsanity.com/paper/2208.12437/) (2022)
- [On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2202.02764/) (2022)

## Abstract (arXiv)

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.