Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological images compared to normal regions. The proposed method outperformed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score

Deep Convolutional Neural Networks for Histological Image Analysis in Gastric Carcinoma Whole Slide Images

Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional neural networks (CNN) have recently gained tremendous attention in general image analysis [3-5]. There has also been an emergence of deep learning in digital histopathology for diverse classification and detection problems [6-8]. The prime motivation behind this work is that no previous study has explored deep learning for the specified goals in gastric cancer WSI. Automated cancer classification can assist pathologists in computer-aided diagnosis in H&E stained WSI without the requirement of IHC staining, thereby reducing preparation and inspection times, and decreasing inter- and intra-observer variability. Necrosis detection can play an important role in prognosis, as larger necrotic areas indicate a smaller chance of survival and vice-versa. Moreover, most deep learning studies have used smaller image sizes mainly due to memory restrictions of GPU, however, we consider larger regions in order to preserve context i.e. neighborhood information and tissue architecture at higher magnification. Further, this method is independent of nuclei segmentation, hence its performance is not limited by segmentation performance as in [1] (evaluation details in [9]). Methods Firstly, standard data augmentation techniques are applied on the available gastric cancer WSI dataset and thousands of images of size 512x512 are generated. Different CNN architectures are empirically studied to observe the behavior of variation in model characteristics (network depth, layer properties, training parameters, etc.) by training them from scratch on a representative subset of whole data for cancer classification. One of these is the Imagenet model [4], however it doesn’t perform desirably on the representative dataset. The self-designed CNN architecture with best classification rates is selected. Later, the proposed CNN is also applied for necrosis detection. Performance is compared with state of the art methods using handcrafted features and random forests. For evaluation, randomized three-fold stratified shuffle split and leave-one-patient-out cross validations are used. Results Conclusion: A self-designed CNN architecture is proposed for image analysis (cancer classification based on IHC and necrosis detection) in H&E stained WSI of gastric cancer. Quantitative evaluation shows that deep learning methods mostly compare favorably to state of the art methods, especially for necrosis detection. In future the aim is to expand the current WSI dataset and to improve the CNN architecture for optimal performance

Automated analysis of colorectal cancer

Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells. The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers

Immunophenotyping in Autoimmune Diseases and Cancer

The cooperation of highly specialized cell types maintains the homeostasis of multicellular organisms. The disturbance of that harmony contributes to the development of several diseases. Most of the cellular functions are executed by proteins, so it is essential to investigate biological processes at the protein level. Antibodies, complex biomolecules with high specificity, are used to recognize our protein of interest in a process known as “immunophenotyping”. One of the routinely used methods to study cellular proteins is flow cytometry, which detects cell surface or intracellular proteins at single-cell resolution. The other most frequent technique is the traditional immunohistochemical investigation of microscopic sections of human tissues. We called authors to publish their latest data studying cancer or autoimmune diseases by immunophenotyping