# Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation

arXiv: https://arxiv.org/abs/2505.22230 · HTML: https://mlsanity.com/paper/2505.22230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [BoxInst: High-Performance Instance Segmentation with Box Annotations](https://mlsanity.com/paper/2012.02310/) (2020)
- [Pointly-Supervised Instance Segmentation](https://mlsanity.com/paper/2104.06404/) (2021)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation](https://mlsanity.com/paper/2210.05174/) (2022)
- [GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification](https://mlsanity.com/paper/2305.18221/) (2023)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2401.11719/) (2024)
- [Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2403.07630/) (2024)
- [Weakly-supervised Medical Image Segmentation with Gaze Annotations](https://mlsanity.com/paper/2407.07406/) (2024)
- [GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy](https://mlsanity.com/paper/2311.08075/) (2023)
- [Gaze-DETR: Using Expert Gaze to Reduce False Positives in Vulvovaginal Candidiasis Screening](https://mlsanity.com/paper/2405.09463/) (2024)
- [From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels](https://mlsanity.com/paper/2408.12814/) (2024)

## Built on by (most cited first, 2 of 2)

- [From Spatial Semantics to Temporal Context: Leveraging Gaze Trajectory for Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.26542/) (2026)
- [GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation](https://mlsanity.com/paper/2609.01310/) (2026)

## Abstract (arXiv)

Weakly supervised semantic segmentation (WSSS) in medical imaging struggles with effectively using sparse annotations. One promising direction for WSSS leverages gaze annotations, captured via eye trackers that record regions of interest during diagnostic procedures. However, existing gaze-based methods, such as GazeMedSeg, do not fully exploit the rich information embedded in gaze data. In this paper, we propose GradTrack, a framework that utilizes physicians' gaze track, including fixation points, durations, and temporal order, to enhance WSSS performance. GradTrack comprises two key components: Gaze Track Map Generation and Track Attention, which collaboratively enable progressive feature refinement through multi-level gaze supervision during the decoding process. Experiments on the Kvasir-SEG and NCI-ISBI datasets demonstrate that GradTrack consistently outperforms existing gaze-based methods, achieving Dice score improvements of 3.21% and 2.61%, respectively. Moreover, GradTrack significantly narrows the performance gap with fully supervised models such as nnUNet.