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(2017) Learning to diagnose from scratch by exploiting dependencies among labels

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Keyword [ChestX-ray14]

Yao L, Poblenz E, Dagunts D, et al. Learning to diagnose from scratch by exploiting dependencies among labels[J]. arXiv preprint arXiv:1710.10501, 2017.


1. Overview

1.1. Motivation

  • pre-trained ImageNet models may introduce unintended biases which are undesirable in a clinical setting
  • there exists dependencies among labels
  • the need for pre-training may be safely removed when there are sufficient medical data available

In this paper

  • DenseNet Image Encoder
  • LSTM Decoder. using LSTM to leverage interdependencies among target lables
  • without pre-training

1.2. Dependency

  • complex interactions between abnormal patterns frequently have significant clinical meaning that provides radiologist with additional context
    • cardiomegaly (心脏扩大) is more like to additionally have pulmonary edema (肺水肿).
    • edema futher predicate the possible presence of both consolidation (实变) and a pleural effusion (积液)

1.3.1. Model Inter-label Dependencies

  • loss function implicitly represent dependencies
  • receive a subset of the previous prediction
  • RNN

1.3.2. OpenI dataset

  • with 7,000 images, smaller and less representative than ChestX-ray8


2. Algorithm

2.1. Model



2.1.1. Encoder



2.1.2. Decoder



  • initial state based on x_enc
  • f_h0, f_c0 with one hidden layer


  • LSTM


  1. y. GT with fixed ordering

2.2. Loss Function




3. Experiments

3.1. Details

  • randomly split. 70%, 10%, 20%
  • ChestX-ray8 noticed insignificant performance difference with different random split
  • 512x512
  • randomly translate 4 directions by 25 pixel
  • randomly rotate [-15, 15]
  • randomly scale [80%, 120%]
  • early stop based on valiadation
  • weighted cross-entropy loss
  • without pre-trained

3.2. Metric

  • NLL
  • AUC
  • DICE
  • Per-example sensitivity and specificity (PESS)
  • Per-class sensitivity and specificity (PCSS)

3.3. Comparison