# A Dual-View Approach to Classifying Radiology Reports by Co-Training

arXiv: https://arxiv.org/abs/2406.05995 · HTML: https://mlsanity.com/paper/2406.05995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)](https://mlsanity.com/paper/2002.06588/) (2020)
- [RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction](https://mlsanity.com/paper/2308.05046/) (2023)
- [RadLing: Towards Efficient Radiology Report Understanding](https://mlsanity.com/paper/2306.02492/) (2023)
- [Co-training an Unsupervised Constituency Parser with Weak Supervision](https://mlsanity.com/paper/2110.02283/) (2021)
- [Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning](https://mlsanity.com/paper/2204.04544/) (2022)
- [MICO: Selective Search with Mutual Information Co-training](https://mlsanity.com/paper/2209.04378/) (2022)

## Abstract (arXiv)

Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods.