# Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning

arXiv: https://arxiv.org/abs/2007.11355 · HTML: https://mlsanity.com/paper/2007.11355/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022)
- [REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening](https://mlsanity.com/paper/2202.08994/) (2022)
- [Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification](https://mlsanity.com/paper/2107.03225/) (2021)
- [Learning self-calibrated optic disc and cup segmentation from multi-rater annotations](https://mlsanity.com/paper/2206.05092/) (2022)
- [SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer](https://mlsanity.com/paper/2206.05763/) (2022)
- [Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring](https://mlsanity.com/paper/2204.08922/) (2022)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [Opinions Vary? Diagnosis First!](https://mlsanity.com/paper/2202.06505/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [An Efficient Person Clustering Algorithm for Open Checkout-free Groceries](https://mlsanity.com/paper/2208.02973/) (2022)
- [ExpNet: A unified network for Expert-Level Classification](https://mlsanity.com/paper/2211.15672/) (2022)

## Abstract (arXiv)

Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts. However, a well trained deep learning model demands a large quantity of properly labeled data, which is relatively expensive since the accurate labeling of glaucoma requires years of specialist training. In order to alleviate this problem, we propose a glaucoma classification framework which takes advantage of not only the properly labeled images, but also undiagnosed images without glaucoma labels. To be more specific, the proposed framework is adapted from the teacher-student-learning paradigm. The teacher model encodes the wrapped information of undiagnosed images to a latent feature space, meanwhile the student model learns from the teacher through knowledge transfer to improve the glaucoma classification. For the model training procedure, we propose a novel training strategy that simulates the real-world teaching practice named as 'Learning To Teach with Knowledge Transfer (L2T-KT)', and establish a 'Quiz Pool' as the teacher's optimization target. Experiments show that the proposed framework is able to utilize the undiagnosed data effectively to improve the glaucoma prediction performance.