Give Me a Knee Radiograph, I Will Show You Where the Knee Joint Space Is: Enforcing Few-Sample Learning for Knee Semantic Segmentation

From the computational perspective, deep learning computer vision methods have already demonstrated very successful applications in a variety of medical image analysis tasks. However, there are several fundamental challenges that stop deep learning methods to obtain their full potential in healthcare settings. One can see that they often need a large column of annotated training data to achieve better accuracy over traditional machine learning methods. The current work tends to use deep few-shot learning to tackle the problem of knee joint space segmentation in plain radiographs using only few samples of manually segmented radiographs.