# Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

arXiv: https://arxiv.org/abs/2108.04423 · HTML: https://mlsanity.com/paper/2108.04423/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 25 of 25)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning](https://mlsanity.com/paper/1909.01804/) (2019)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training](https://mlsanity.com/paper/1804.02586/) (2018)
- [Semi-Supervised Learning with Scarce Annotations](https://mlsanity.com/paper/1905.08845/) (2019)
- [Deep semi-supervised segmentation with weight-averaged consistency targets](https://mlsanity.com/paper/1807.04657/) (2018)
- [GraphXNET- Chest X-Ray Classification Under Extreme Minimal Supervision](https://mlsanity.com/paper/1907.10085/) (2019)
- [Semi-supervised Medical Image Classification with Global Latent Mixing](https://mlsanity.com/paper/2005.11217/) (2020)
- [Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space](https://mlsanity.com/paper/1907.09607/) (2019)
- [Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training](https://mlsanity.com/paper/2012.01166/) (2020)
- [Semi-Supervised Multi-Task Learning With Chest X-Ray Images](https://mlsanity.com/paper/1908.03693/) (2019)
- [Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images](https://mlsanity.com/paper/1812.07832/) (2018)
- [A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset](https://mlsanity.com/paper/2010.01947/) (2020)

## Built on by (most cited first, 5 of 5)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification](https://mlsanity.com/paper/2209.01858/) (2022)
- [SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification](https://mlsanity.com/paper/2307.04610/) (2023)
- [Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model](https://mlsanity.com/paper/2606.01698/) (2026)
- [Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets](https://mlsanity.com/paper/2505.14846/) (2025)

## Abstract (arXiv)

Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled datasets alongside larger unlabeled datasets and offer potential for reducing labeling cost. In this work, we introduce NoTeacher, a novel consistency-based SSL framework which incorporates probabilistic graphical models. Unlike Mean Teacher which maintains a teacher network updated via a temporal ensemble, NoTeacher employs two independent networks, thereby eliminating the need for a teacher network. We demonstrate how NoTeacher can be customized to handle a range of challenges in radiology image classification. Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data. In realistic empirical evaluations on three public benchmark datasets spanning the workhorse modalities of radiology (X-Ray, CT, MRI), we show that NoTeacher achieves over 90-95% of the fully supervised AUROC with less than 5-15% labeling budget. Further, NoTeacher outperforms established SSL methods with minimal hyperparameter tuning, and has implications as a principled and practical option for semisupervised learning in radiology applications.