Learning to diagnose from scratch by exploiting dependencies among labels

Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman

Abstract: The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies. Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset. This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks. We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training. Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.

TL;DR: we present the state-of-the-art results of using neural networks to diagnose chest x-rays

Keywords: medical diagnosis, medical imaging, multi-label classification

Code: [ yaoli/chest_xray_14](https://github.com/yaoli/chest_xray_14) + [ 9 community implementations](https://paperswithcode.com/paper/?openreview=H1uP7ebAW)

Data: [ChestX-ray8](https://paperswithcode.com/dataset/chestx-ray8), [ImageNet](https://paperswithcode.com/dataset/imagenet)

Community Implementations: [ 7 code implementations](https://www.catalyzex.com/paper/arxiv:1710.10501/code)