Automatically Designing CNN Architectures for Medical Image Segmentation

TL;DR

This paper introduces an automatic hyperparameter optimization method using reinforcement learning to design CNN architectures for medical image segmentation, achieving state-of-the-art results efficiently.

Contribution

It presents a novel reinforcement learning algorithm for automatic CNN architecture design tailored for medical image segmentation tasks.

Findings

Achieved state-of-the-art segmentation accuracy on cardiac MRI images.

Reduced computational cost compared to existing methods.

Demonstrated effectiveness of automatic hyperparameter tuning in medical imaging.

Abstract

Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources. To address this tedious problem, we propose a novel algorithm to optimally find hyperparameters of a deep network architecture automatically. We specifically focus on designing neural architectures for medical image segmentation task. Our proposed method is based on a policy gradient reinforcement learning for which the reward function is assigned a segmentation evaluation utility (i.e., dice index). We show the efficacy of the proposed method with its low computational cost in comparison with the state-of-the-art medical image segmentation networks. We also present a new architecture design, a densely connected encoder-decoder CNN, as a strong baseline architecture to apply the proposed hyperparameter search algorithm. We apply the proposed algorithm to each layer of the baseline architectures. As an application, we train the proposed system on cine cardiac MR images from Automated Cardiac Diagnosis Challenge (ACDC) MICCAI 2017. Starting from a baseline segmentation architecture, the resulting network architecture obtains the state-of-the-art results in accuracy without performing any trial-and-error based architecture design approaches or close supervision of the hyperparameters changes.