FEW SHOT LEARNING IN HISTOPATHOLOGICAL IMAGES: REDUCING THE NEED OF LABELED DATA ON BIOLOGICAL DATASETS
Authors: Medela A., Picon A., Saratxaga CL., Beim O., Cabezon V., Cicchi R., Bilbao R., Glover B.
Autors Affiliation: Tecnalia Res & Innovat, Comp Vis, Derio, Spain; Fdn Vasca Innovac & Invest Sanitarias, BIOEF, Baracaldo, Spain; CNR, Natl Inst Opt, Sesto Fiorentino, Italy; European Lab Nonlinear Spect, LENS, Sesto Fiorentino, Italy; Imperial Coll, London, England
Abstract: Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training. Generating such extensive and well labelled datasets is time consuming and is not feasible for certain tasks and so, most of the medical datasets available are scarce in images and therefore, not enough for training. In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images.
Our results show that our few-shot approach is able to obtain a balanced accuracy (BAC) of 90 % with just 60 training images, even for the Lung and Breast tissues that were not present on the training set. This outperforms the fine-tune transfer learning approach that obtains 73 % BAC with 60 images and requires 600 images to get up to 81 % BAC.
KeyWords: Histopathology analysis; few shot learning; convolutional neural network; domain adaptation; optical biopsy