The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Algorithm Selection in Multimodal Medical Image Registration

Medical image acquisition technology has improved significantly throughout the last several decades, and clinicians now rely on medical images to diagnose illnesses, and to determine treatment protocols, and surgical planning. Medical images have been divided by researchers into two types of structures: functional and anatomical. Anatomical imaging, such as magnetic resonance imaging (MRI), computed tomography imaging (C.T.), ultrasound, and other systems, enables medical personnel to examine a body internally with great accuracy, thereby avoiding the risks associated with exploratory surgery. Functional (or physiological) imaging systems contain single-photon emission computed tomography (SPECT), positron emission tomography (PET), and other methods, which refer to a medical imaging system for discovering or evaluating variations in absorption, blood flow, metabolism, and regional chemical composition. Notably, one of these medical imaging models alone cannot usually supply doctors with adequate information. Additionally, data obtained from several images of the same subject generally provide complementary information via a process called medical image registration. Image registration may be defined as the process of geometrically mapping one -image’s coordinate system to the coordinate system of another image acquired from a different perspective and with a different sensor. Registration performs a crucial role in medical image assessment because it helps clinicians observe the developing trend of the disease and make proper measures accordingly. Medical image registration (MIR) has several applications: radiation therapy, tumour diagnosis and recognition, template atlas application, and surgical guidance system. There are two types of registration: manual registration and registration-based computer system. Manual registration is when the radiologist /physician completes all registration tasks interactively with visual feedback provided by the computer system, which can result in serious problems. For instance, investigations conducted by two experts are not identical, and registration correctness is determined by the user's assessment of the relationship between anatomical features. Furthermore, it may take a long time for the user to achieve proper alignment, and the outcomes vary according to the user. As a result, the outcomes of manual alignment are doubtful and unreliable. The second registration approach is computer-based multimodal medical image registration that targets various medical images, and an arraof application types. . Additionally, automatic registration in medical pictures matches the standard recognized characteristics or voxels in pre- and intra-operative imaging without user input. Registration of multimodal pictures is the initial step in integrating data from several images. Automatic image processing has emerged to mitigate (Husein, do you mean “mitigate” or “improve”?) the manual image registration reliability, robustness, accuracy, and processing time. While such registration algorithms offer advantages when applied to some medical images, their use with others is accompanied by disadvantages. No registration technique can outperform all input datasets due to the changeability of medical imaging and the diverse demands of applications. However, no algorithm is preferable under all possible conditions; given many available algorithms, choosing the one that adapts the best to the task is vital. The essential factor is to choose which method is most appropriate for the situation. The Algorithm Selection Problem has emerged in numerous research disciplines, including medical diagnosis, machine learning, optimization, and computations. The choice of the most powerful strategy for a particular issue seeks to minimize these issues. This study delivers a universal and practical framework for multimodal registration algorithm choice. The primary goal of this study is to introduce a generic structure for constructing a medical image registration system capable of selecting the best registration process from a range of registration algorithms for various used datasets. Three strategies were constructed to examine the framework that was created. The first strategy is based on transforming the problem of algorithm selection into a classification problem. The second strategy investigates the effect of various parameters, such as optimization control points, on the optimal selection. The third strategy establishes a framework for choosing the optimal registration algorithm for a delivered dataset based on two primary criteria: registration algorithm applicability, and performance measures. The approach mentioned in this section has relied on machine learning methods and artificial neural networks to determine which candidate is most promising. Several experiments and scenarios have been conducted, and the results reveal that the novel Framework strategy leads to achieving the best performance, such as high accuracy, reliability, robustness, efficiency, and low processing time

Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis