Evolutionary Deep Convolutional Neural Networks for Medical Image Analysis

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Embargoed until 2022-09-23
Copyright: Hassan Zadeh Koohi, Tahereh
Medical image segmentation is a procedure to analyse an image’s content to find an organ, cancer, tumour, or possible abnormalities. Since hospitals and medical centres generate billions of images daily worldwide, manual analysis of the images is frustrating. Therefore there is a need to improve automatic techniques to examine the content of images. Deep Convolutional Neural Networks (DCNNs) are one of the most reliable and successful approaches to analyse images’ content. However, the main problem is a lack of rules to design a network, and trial and error is the usual approach to find a network structure along with its training parameters. Regarding the diversity of medical images, existing with various types of noises and artefacts, the limited number of available labelled medical images, and limited available computational facilities, designing a CNN for medical image analysis is even more complicated. Because of the importance of medical image segmentation, during the last decade, various CNNs are designed manually; however, most of these networks work well for the segmentation of a specific dataset or application. One of the solutions to address this problem is developing networks automatically. Neuroevolution, which is the combination of an evolutionary algorithm and Neural Networks (NNs), can automatically design a network. Evolutionary algorithms are relatively easy to understand and implement; however, they need considerable computation to evolve a network. Since Nerouevolution is computationally demanding, there is very limited previous work regarding applying Neuroevolution for medical image segmentation. Existing works just set up a part of the parameters to develop a network and have been applied to a limited number of datasets. The most significant drawbacks of existing works are lack of robustness and generalizability; also, most of them are computationally expensive. In this thesis, several Neroevolutionary-based frameworks are developed for 2D and 3D medical image segmentation. Firstly, a new block-based encoding model is developed to generate variable length 2D Deep Convolutional Neural Networks (DCNNs). The proposed encoding model could find appropriate values for several hyperparameters to create and train a DCNN. Also, a Genetic Algorithm (GA) is employed to evolve the generated networks. Besides, a comprehensive analysis is done to find an appropriate population size and generations, and consequently, an improved model is developed. In addition, to improve the results’ quality, an ensemble of found networks is utilised for final segmentation. Then to find a 3D evolutionary network, two approaches are examined. According to the proposed 2D model, a 3D model is developed to generate a population of 3D networks and evolve the 3D networks to find an appropriate 3D network for 3D medical image segmentation. Since evolving 3D networks is computationally expensive, a second approach is also introduced. In the second approach, the possibility of using a 2D evolutionary model to create a 3D network is examined and named Converted 3D network. Because of the diversity of medical images and the complexity of medical image analysis, sometimes more complicated CNN is needed. To address this issue, also another evolutionary model is developed in this thesis to generate more accurate and complex DCNNs using the combination of Dense and Residual blocks. In the proposed DenseRes model, a new encoding model is introduced, which is able to create a variable-length network with variable filter sizes within a block. In the DenseRes model, all required parameters to generate and train a network are included in the search. Most of the time, the Region Of Interest (ROI) is a small part of a medical image with almost the similar colour and texture of the surrounding organs. Therefore, more precise network architectures, like attention networks, are needed to process the images. To do so, two different approaches are introduced in this thesis to develop evolutionary attention networks. First, a 2D evolutionary attention model is proposed that is able to find an appropriate attention gate to transfer the block’s input to its output. Since some useful information will be lost during the downsampling in DCNNs, another 2D and 3D evolutionary attention framework is introduced to address this issue. In this model, besides creating a network structure along with its training parameters, an evolutionary algorithm is employed to find an appropriate model to recover and transfer feature maps from downsampling to the upsampling part of a network. The effectiveness of the proposed models is examined using various publicly available datasets. Results are compared with multiple manual and automatically designed models. The significant findings of this thesis can summarise as: (1) the proposed models obtain much better segmentation accuracy compared to state-of-the-art models, also, the proposed models are computationally cheap, even for developing 3D evolutionary networks; (2) converting a 2D evolutionary model to a 3D model is a reliable, fast, and accurate approach to create 3D networks; (3) including more constructive parameters in the search space can lead to more precise networks; (4) the initial population plays a significant role in the final results and decreasing training time; moreover, using variable filter sizes within a block can obtain better results compared to using a fixed one; (5) recovering a downsampling’s feature maps and transferring them to the corresponding upsampling part can considerably improve segmentation accuracy; (6) the proposed models are robust and general such that they can be applied for the segmentation of various medical images (CT and MRI) for different organs and tumour segmentation; (7) all the proposed encoding models are compatible with conventional crossover and mutation techniques, without any extra effort to create a new crossover technique or using a method to check the correctness of layers’ sequences.
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Hassan Zadeh Koohi, Tahereh
Essam, Daryl
Sarker, Ruhul
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PhD Doctorate
UNSW Faculty
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