DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
This addresses the problem of adapting medical image segmentation models across datasets with limited labeled data, representing an incremental improvement in domain-specific techniques.
The paper tackles domain adaptation for retinal vessel segmentation by proposing DASA, a two-stage method using systematic dropout in stacked autoencoders, which improved logloss from 0.40 to 0.18 and ROC AUC from 0.86 to 0.92 when adapting from DRIVE to STARE datasets.
Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics of samples in both domains are dissimilar. The task of directly training or adapting a learner in the target domain is challenged by lack of abundant labeled samples. In this paper we propose a technique for domain adaptation in stacked autoencoder (SAE) based deep neural networks (DNN) performed in two stages: (i) unsupervised weight adaptation using systematic dropouts in mini-batch training, (ii) supervised fine-tuning with limited number of labeled samples in target domain. We experimentally evaluate performance in the problem of retinal vessel segmentation where the SAE-DNN is trained using large number of labeled samples in the source domain (DRIVE dataset) and adapted using less number of labeled samples in target domain (STARE dataset). The performance of SAE-DNN measured using $logloss$ in source domain is $0.19$, without and with adaptation are $0.40$ and $0.18$, and $0.39$ when trained exclusively with limited samples in target domain. The area under ROC curve is observed respectively as $0.90$, $0.86$, $0.92$ and $0.87$. The high efficiency of vessel segmentation with DASA strongly substantiates our claim.