Multitask Learning or Transfer Learning? Application to Cancer Detection

Stephen Obonyo, Daniel Ruiru

Abstract

Multitask Learning (MTL) and Transfer Learning (TL) are two key Machine Learning (ML) approaches which have been widely adopted to improve model’s performance. In Deep Learning (DL) context, these two learning methods have contributed to competitive results in various areas of application even if the size of dataset is relatively small. While MTL involves learning from a key task and other auxiliary tasks simultaneously and sharing signals among them, TL focuses on the transfer of knowledge from already existing solution within the same domain. In this paper, we present MTL and TL based models and their application to Invasive Ductal Carcinoma (IDC) detection. During training, the key learning task in MTL was detection of IDC whereas skin and brain tumor were auxiliary tasks. On the other hand, the TL-based model was trained on skin cancer dataset and the learned representations transferred in order to detect IDC. The accuracy difference between MTL-based model and TL-based model on IDC detection was 8.6% on validation set and 9.37% on training set. On comparing the loss metric of the same models, a cross entropy of 0.18 and 0.08 was recorded on validation set and training set respectively.

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