Computational Models for Automated Histopathological Assessment of Colorectal Liver Metastasis Progression

PhDHistopathology imaging is a type of microscopy imaging commonly used for the microlevel clinical examination of a patient’s pathology. Due to the extremely large size of histopathology images, especially whole slide images (WSIs), it is difficult for pathologists to make a quantitative assessment by inspecting the details of a WSI. Hence, a computeraided system is necessary to provide a subjective and consistent assessment of the WSI for personalised treatment decisions. In this thesis, a deep learning framework for the automatic analysis of whole slide histopathology images is presented for the first time, which aims to address the challenging task of assessing and grading colorectal liver metastasis (CRLM). Quantitative evaluations of a patient’s condition with CRLM are conducted through quantifying different tissue components in resected tumorous specimens. This study mimics the visual examination process of human experts, by focusing on three levels of information, the tissue level, cell level and pixel level, to achieve the step by step segmentation of histopathology images. At the tissue level, patches with category information are utilised to analyse the WSIs. Both classification-based approaches and segmentation-based approaches are investigated to locate the metastasis region and quantify different components of the WSI. For the classification-based method, different factors that might affect the classification accuracy are explored using state-of-the-art deep convolutional neural networks (DCNNs). Furthermore, a novel network is proposed to merge the information from different magnification levels to include contextual information to support the final decision. With the support by the segmentation-based method, edge information from the image is integrated with the proposed fully convolutional neural network to further enhance the segmentation results. At the cell level, nuclei related information is examined to tackle the challenge of inadequate annotations. The problem is approached from two aspects: a weakly supervised nuclei detection and classification method is presented to model the nuclei in the CRLM by integrating a traditional image processing method and variational auto-encoder (VAE). A novel nuclei instance segmentation framework is proposed to boost the accuracy of the nuclei detection and segmentation using the idea of transfer learning. Afterwards, a fusion framework is proposed to enhance the tissue level segmentation results by leveraging the statistical and spatial properties of the cells. At the pixel level, the segmentation problem is tackled by introducing the information from the immunohistochemistry (IHC) stained images. Firstly, two data augmentation approaches, synthesis-based and transfer-based, are proposed to address the problem of insufficient pixel level segmentation. Afterwards, with the paired image and masks having been obtained, an end-to-end model is trained to achieve pixel level segmentation. Secondly, another novel weakly supervised approach based on the generative adversarial network (GAN) is proposed to explore the feasibility of transforming unpaired haematoxylin and eosin (HE) images to IHC stained images. Extensive experiments reveal that the virtually stained images can also be used for pixel level segmentation

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available