A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

Topology and attention in computational pathology

Histopathology serves as the gold standard in the process of cancer diagnosis and unravelling the disease heterogeneity. In routine practice, a trained histopathologist performs visual examination of tissue glass slides under the microscope. The objective of the visual examination is to observe the morphological appearance of tissue sections, analyse the density of tumour rich areas, spatial arrangement, and architecture of diferent types of cells. However, careful visual examination of tissue slides is a demanding task especially when workloads are high, and the subjective nature of the histological grading inevitably leads to inter- and even intra-observer variability. Attaining high accuracy and objective quantification of tissue specimens in cancer diagnosis are some of the ongoing challenges in modern histopathology. With the recent advent of digital pathology, tissue glass slides can now be scanned with digital slides scanners to produce whole slide images (WSIs). A WSI contains a high-resolution pixel representation of tissue slide, stored in a pyramidal structure and typically containing 1010 pixels. Automated algorithms are generally based on the concepts of digital image analysis which can analyse WSIs to improve the precision and reproducibility in cancer diagnostics. The reliability of the results of an algorithm can be objectively measured and improved against an objective standard. In this thesis, we focus on developing automated methods for quantitative assessment of histology WSIs with the aim of improving the precision and reproducibility of cancer diagnosis. More specifically, the designed automated computational pathology algorithms are based on deep learning models in conjunction with algebraic topology and visual attention mechanisms. To the best of our knowledge, the applicability of attention and topology based methods have not been explored in the domain of computational pathology. In this regard, we propose an algorithm for computing persistent homology profiles (topological features) and propose two variants for effective and reliable tumour segmentation of colorectal cancer WSIs. We show that incorporation of deep features along with topological features improves the overall performance for tumour segmentation. We then present the first-ever systematic study (contest) for scoring the human epidermal growth factor receptor 2 (HER2) biomarker on breast cancer histology WSIs. Further, we devise a reinforcement learning based attention mechanism for HER2 scoring that sequentially identifies and analyses the diagnostically relevant regions within a given image, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. We demonstrate the proposed model outperforms other methods participated in our systematic study, most of them were using state-of-the-art deep convolutional networks. Finally, we propose a multi-task learning framework for simultaneous cell detection and classifi- cation, which we named as Hydra-Net. We then compute an image based biomarker which we refer as digital proximity signature (DPS), to predict overall survival in diffuse large B-cell lymphoma (DLBCL) patients. Our results suggest that patients with high collagen-tumour proximity are likely to experience better overall survival

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments