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(CVPR 2018) Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

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Keyword [ChestX-ray14]

Li Z, Wang C, Han M, et al. Thoracic disease identification and localization with limited supervision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8290-8299.


1. Overview

1.1. Motivation

  • Medical training data rarely includes more than global image-level labels as segmentations are time-consuming and expensive to collect

In this paper, it proposes a architecture

  • learn at multiple resolutions while generating saliency maps with weak supervision
  • parameterize the Log-Sum-Exp pooling function with a learnable lower-bounded adaptation (LSE-LBA)

1.2. Disease

  • Enlargement. width of the heart is measured to be 50% or greater than the width of the thoracic cage
  • Nodules (肺结节). subtle findings as small as a few millimeters in size and are frequently missed by practitioners even when viewed closely on a high resolution monitor
  • Diffuse infiltrative opacification (浸润). in the periphery of the lung is often more easily noted from a global view. But closer inspection of the anomalous region is often required to narrow the differential diagnosis and determine followup

1.3. Contribution

  • multi-resolution with MIL
  • LSE-LBA


  • Wang. ChestX-ray14 (one resolution, non-adaptive pooling)
  • Li. upsample or downsample to PxP grids
  • Chexnet
  • Yao. leverage interdependencies among 14 diseases
  • Guan. attention guided CNN
  • Kumar. cascaded multiple predictions
  • Tienet. additional radiology reports


2. Methods

2.1. Network



  • ResNet line. f identity connection



  • enseNet line



  • coarse-to-fine. U denote upsample



2.2. Pooling Function

  • Noisy-OR (NOR)



  • Generalized-mean (GM)



  • Log-Sum-Exp



  • Log-Sum-Exp Pooling with Lower-bound Adaptation (LSE-LBA)



  1. larger r_0 encourages the learned saliency map to have less diffuse modes.


3. Experiments

3.1. dataset

  • 1024x1024 to 512x512, [0, 1]
  • data augmentation
  • resize [0.25, 0.75]
  • translate in for direction [-50, 50]
  • rotate [-25, 25]
  • not use other clinical information, such as age and gender
  • official split


3.2. Comparison



  • when r_0 is large, the performance of disffused abnormalities degrades, such as atelectasis(扩张不全), cardiomegaly (心脏扩大), effusion (积液) and pneumonia (肺炎)


  • increasing r_0 results in overall sharper saliency maps