TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.Comment: 9 page

Pattern Recognition Software and Techniques for Biological Image Analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays

Born This Way: Using Intrinsic Disorder to Map the Connections Between SLITRKs, TSHR, and Male Sexual Orientation

Recently, genome-wide association study reveals a significant association between specific single nucleotide polymorphisms (SNPs) in men and their sexual orientation. These SNPs (rs9547443 and rs1035144) reside in the intergenic region between the SLITRK5 and SLITRK6 genes and in the intronic region of the TSHR gene and might affect functionality of SLITRK5, SLITRK6, and TSHR proteins that are engaged in tight control of key developmental processes, such as neurite outgrowth and modulation, cellular differentiation, and hormonal regulation. SLITRK5 and SLITRK6 are single-pass transmembrane proteins, whereas TSHR is a heptahelical G protein-coupled receptor (GPCR). Mutations in these proteins are associated with various diseases and are linked to phenotypes found at a higher rate in homosexual men. A bioinformatics analysis of SLITRK5, SLITRK6, and TSHR proteins is conducted to look at their structure, protein interaction networks, and propensity for intrinsic disorder. It is assumed that this information might improve understanding of the roles that SLITRK5, SLITRK6, and TSHR play within neuronal and thyroidal tissues and give insight into the phenotypes associated with male homosexuality