Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

Algorithm 1

Proposed deep AL framework.

	Input:
	: initial labeled training data, composed of samples;
	: initial unlabeled training data, composed of m samples;
	: committee of medical image segmentation networks to be trained;
	Output:
	: trained committee of medical image segmentation networks
	Repeat:
	1. Train with the loss function in equation (4) on the labeled data.
	2. Calculate the dissimilarity of each sample in U among every member in C and select the data with the larger dissimilarity.
	3. Oracle is queried to annotate the data selected in step 2 and add the annotated sample to L.
	4. Update U and L.
	Until:
	The is converged to a satisfied result.