Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Super Short Introduction
- Paper Link
- This paper proposes a novel CNN network tailored for predicting Tuberculosis from X-Ray images. Highlights of the paper is that it achieves comparable performance w.r.t other approaches while having a super small architecture and using much small training data. This also gives the advantage of speed. Another highlight is intuitive visualization especially with saliency map which looks quite helpful for medical professionals.
Overview of Main Ideas
Definitions
- Saliency Map: Derivative of output class score w.r.t input image. It has the same resolution as the input image. It shows relative importance of input pixels in the decision making.
- grad-CAMs: Derivative of output class score w.r.t a feature map from a deeper layer. It has lower resolution than input image but has better localization.
Network Structure
Architecture is shown below. \(a * b/c\) format is used in multiple blocks shown below where a and be are the spatial kernel dimensions and c is the stride. \(\bigoplus\) means addition and GAP is global average pooling.
One interesting this about this architecture is the higher stride (4) in the first block. Paper notes that performance did not degrade when stride was increased from 2 to 4. Paper suggests that this may be due to the fact that it is easier to compute simple features like edges (which is what first few layers do) and so strided convolutions did not degrade the performance.
Visualization
Paper claims that Saliency maps were visually quite intuitive (one of the authors is expert in inspection of X-ray images). Lower resolution in case of grad-CAMs made them less useful. Motivation for visualization is to give the clinician another piece of information besides the classification’s prediction values. As seen below, it is easy to agree with the paper that the saliency maps have important visual queues which the clinicians would understand.
