Siamese Networks with Soft Labels for Unsupervised Lesion Detection and
  Patch Pretraining on Screening Mammograms
Kevin Van Vorst Li Shen
Self-supervised learning has become a popular way to pretrain a deep learning
model and then transfer it to perform downstream tasks. However, most of these
methods are developed on large-scale image datasets that contain natural
objects with clear textures, outlines, and distinct color contrasts. It remains
uncertain whether these methods are equally effective for medical imaging,
where the regions of interest often blend subtly and indistinctly with the
surrounding tissues. In this study, we propose an alternative method that uses
contralateral mammograms to train a neural network to encode similar embeddings
when a pair contains both normal images and different embeddings when a pair
contains normal and abnormal images. Our approach leverages the natural
symmetry of human body as weak labels to learn to distinguish abnormal lesions
from background tissues in a fully unsupervised manner. Our findings suggest
that it's feasible by incorporating soft labels derived from the Euclidean
distances between the embeddings of the image pairs into the Siamese network
loss. Our method demonstrates superior performance in mammogram patch
classification compared to existing self-supervised learning methods. This
approach not only leverages a vast amount of image data effectively but also
minimizes reliance on costly labels, a significant advantage particularly in
the field of medical imaging.