4th Workshop on human activity sensing corpus and applications: towards open-ended context awareness

Current motion sensors in wearable devices are primarily used for simple orientation and motion sensing. They provide however signals related to more complex and subtle human behaviours which will enable next-generation human-oriented computing in scenarios of high societal value. This requires large scale human activity corpuses and improved methods to recognise activities and their context. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing robust activity and context recognition methods and evaluating systems in the real world. As a special topic, we wish to reflect on the challenges and approaches to recognise activities outside of a pre-defined set to achieve an open-ended activity and context awareness. Following the success of previous years, this workshop is the place to share experiences on human activity corpus and their applications and to discuss the future of activity sensing, in particular towards open-ended contextual intelligence

Reproducibility Study of Tumor Biomarkers Extracted from Positron Emission To-mography Images with 18F-Fluorodeoxyglucose

Introduction and aim Cancer is one of the main causes of death worldwide. Tumor diagnosis, staging, surveillance, prognosis and access to the response to therapy are critical when it comes to plan and analyze the optimal treatment strategies of cancer diseases. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging has provided some reliable prognostic factors in several cancer types, by extracting quantitative measures from the images obtained in clinics. The recent addition of digital equipment to the clinical armamentarium of PET leads to some concerns regarding inter-device data variability. Consequently, the reproducibility assess-ment of the tumor features, usually used in clinics and research, extracted from images acquired in an analog and new digital PET equipment is of paramount importance for use of multi-scanner studies in longitudinal patient’s studies. The aim of this study was to evaluate the inter-equipment reliability of a set of 25 lesional features commonly used in clinics and research. Material and methods In order to access the features agreement, a dual imaging protocol was designed. Whole-body 18F-FDG PET images from 53 oncological patients were acquired, after a single 18F-FDG injection, with two devices alternatively: Philips Vereos Digital PET/CT (VE-REOS with three different reconstruction protocols- digital) and Philips GEMINI TF-16 (GEM-INI with single standard reconstruction protocol- analog). A nuclear medicine physician identi-fied 283 18F-FDG avid lesions. Then, all lesions (both equipment) were automatically segmented based on a Bayesian classifier optimized to this study. In the total, 25 features (first order statistics and geometric features) were computed and compared. The intraclass correlation coefficient (ICC) was used as measure of agreement. Results A high agreement (ICC > 0.75) was obtained for most of the lesion features pulled out from both devices imaging data, for all (GEMINI vs VEREOS) reconstructions. The lesion fea-tures most frequently used, maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis reached maximum ICC of 0.90, 0.98 and 0.97, respectively. Conclusions Under controlled acquisition and reconstruction parameters, most of the features studied can be used for research and clinical work, whenever multiple scanner (e.g. VEREOS and GEMINI) studies, mainly during longitudinal patient evaluation, are used

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére