自分の書いた英文学術書籍の概要x3

Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology

Learning classifier systems (LCS) are algorithms that incorporate genetic algorithms with reinforcement learning to produce adaptive systems described by if-then rules. As a new interdisciplinary branch of biology, synthetic biology pursues the design and construction of complex artificial biological systems from the bottom-up. A new trend is growing in designing artificial metabolic pathways that show previously undescribed reactions produced by the assembly of enzymes from different sources in a single host. However, few researchers have succeeded thus far because of the difficulty of analyzing gene expression. To tackle this problem, data mining and knowledge discovery are essential. In this context, nature-inspired LCS are well suited to extracting knowledge from complex systems and thus can be exploited to investigate and utilize natural biological phenomena. This chapter focuses on applying LCS to gene expression analysis in synthetic biology. Specifically, it describes the optimization of artificial operon structure for the biosynthesis of metabolic pathway products in Escherichia coli. This optimization is achieved by manipulating the order of multiple genes within the artificial operons.

Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection

Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced Magnetic Resonance (MR) images---realistic but completely different from the original ones---using Generative Adversarial Networks (GANs). This study exploits Progressive Growing of GANs (PGGANs), a multi-stage generative training method, to generate original-sized 256×256 MR images for convolutional neural network-based brain tumor detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of tumors in size, location, shape, and contrast. Our preliminary results show that this novel PGGAN-based DA method can achieve promising performance improvement, when combined with 

CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study

Prostate cancer is expected to be the most common cancer among men in the USA during 2018. However, prostate imaging is still challenging despite the advancements in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions. Unfortunately, the boundary between the CG and PZ is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of Convolutional Neural Networks (CNNs) on two different MRI prostate datasets. Especially, we implemented and tested three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in $4$-fold cross-validation.
In general, U-Net outperforms the other methods, especially when sufficient training data---with reliable and descriptive features about the domain of interest---are provided.