0%

(CVPR 2018) Thoracic Disease Identification and Localization with Limited Supervision

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

  • building a highly accurate prediction model requires a large number of annotation and finding the site of abnormalities

In this paper, it proposes a method that can work well with a small amount of location annotations

  • effectively leverage both class information and limited location annotation
  • perform disease identificaion and localization at the same time
  • slice the image into patch grids to capture the local information of the disease

1.2. Disease

  • large object. Cardiomegaly (心脏扩大), Emphysema (肺气肿), Pneumothorax (气胸)
  • small object. Mass (肺部块), Nodule (肺结节)
  • Fibrosis (肺纤维化)
  • Edema (肺水肿)
  • Consolidation (肺实变)
  • Atelectasis (肺扩张不全)
  • Effusion (肺积液)
  • Infiltration (肺部浸润)
  • Pneumonia (肺炎)
  • Hernia (肺氙)
  • Pleural Thickening (肺膜增厚)

1.3. Model



  • feature from ResNet-v2 before global pooling layer
  • upsample (bilinear interpolation) or downsample (max-pooling) (PxPxc*)
  • FCN (PxPxK)

1.4. Loss Function

1.4.1. With Annotated Bounding Box



  • i-th image, j-th grid, k-th channel (class)

1.4.2. Without Annotated Bounding Box



1.4.3. Loss for k-th Class



  • *. GT


1.4.4. Simplify to



1.4.5. For All Class





2. Experiments


2.1. Details

  • patch slice. {12, 16, 20}
  • λ_bbox. 5
  • normalize the patch scores p and 1-p from [0, 1] to [0.98, 1]

2.2. Dataset

  • 112,120 images with 14 disease labels
  • 984 bounding boxes for 880 image about 8 disease
  • annotated 880 images vs unannotated 111,240 images
  • Image. 512x512, [-1, 1]
  • no data augmentation

2.3. Classification

  • train. 70% annotated + 70% unannotated
  • Metric. AUC