Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) can be unified under a single generalization of assigning a label, from an predefined set, to each pixel in an image. We first formalize this generalization and then present two probabilistic models capable of solving it. The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. The second model is based on conditional random fields (CRFs), where dependencies between pixels are defined using a graph structure. Furthermore, we demonstrate how supervised learning and an appropriate training set can be used to automatically determine all model parameters. We evaluate both models' ability to segment a challenging dataset consisting of 116 images and compare our results to 5 previously published methods

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) canbe unified under a single generalization of assigning a label, from an predefinedset, to each pixel in an image. We first formalize this generalizationand then present two probabilistic models capable of solving it. The firstmodel is based on independent pixel labeling using maximum a-posteriori(MAP) estimation. The second model is based on conditional randomfields (CRFs), where dependencies between pixels are defined using agraph structure. Furthermore, we demonstrate how supervised learningand an appropriate training set can be used to automatically determineall model parameters. We evaluate both models\u27 ability to segment achallenging dataset consisting of 116 images and compare our results to5 previously published methods

Advances in automated tongue diagnosis techniques

This paper reviews the recent advances in a significant constituent of traditional oriental medicinal technology, called tongue diagnosis. Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system

Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data

Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to predict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algorithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations

Mitmekesiste bioloogiliste andmete ühendamine ja analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTänu tehnoloogiate arengule on bioloogiliste andmete maht viimastel aastatel mitmekordistunud. Need andmed katavad erinevaid bioloogia valdkondi. Piirdudes vaid ühe andmestikuga saab bioloogilisi protsesse või haigusi uurida vaid ühest aspektist korraga. Seetõttu on tekkinud üha suurem vajadus masinõppe meetodite järele, mis aitavad kombineerida eri valdkondade andmeid, et uurida bioloogilisi protsesse tervikuna. Lisaks on nõudlus usaldusväärsete haigusspetsiifiliste andmestike kogude järele, mis võimaldaks vastavaid analüüse efektiivsemalt läbi viia. Käesolev väitekiri kirjeldab, kuidas rakendada masinõppel põhinevaid integratsiooni meetodeid erinevate bioloogiliste küsimuste uurimiseks. Me näitame kuidas integreeritud andmetel põhinev analüüs võimaldab paremini aru saada bioloogilistes protsessidest kolmes valdkonnas: Alzheimeri tõbi, toksikoloogia ja immunoloogia. Alzheimeri tõbi on vanusega seotud neurodegeneratiivne haigus millel puudub efektiivne ravi. Väitekirjas näitame, kuidas integreerida erinevaid Alzheimeri tõve spetsiifilisi andmestikke, et moodustada heterogeenne graafil põhinev Alzheimeri spetsiifiline andmestik HENA. Seejärel demonstreerime süvaõppe meetodi, graafi konvolutsioonilise tehisnärvivõrgu, rakendamist HENA-le, et leida potentsiaalseid haigusega seotuid geene. Teiseks uurisime kroonilist immuunpõletikulist haigust psoriaasi. Selleks kombineerisime patsientide verest ja nahast pärinevad laboratoorsed mõõtmised kliinilise infoga ning integreerisime vastavad analüüside tulemused tuginedes valdkonnaspetsiifilistel teadmistel. Töö viimane osa keskendub toksilisuse testimise strateegiate edasiarendusele. Toksilisuse testimine on protsess, mille käigus hinnatakse, kas uuritavatel kemikaalidel esineb organismile kahjulikke toimeid. See on vajalik näiteks ravimite ohutuse hindamisel. Töös me tuvastasime sarnase toimemehhanismiga toksiliste ühendite rühmad. Lisaks arendasime klassifikatsiooni mudeli, mis võimaldab hinnata uute ühendite toksilisust.A fast advance in biotechnological innovation and decreasing production costs led to explosion of experimental data being produced in laboratories around the world. Individual experiments allow to understand biological processes, e.g. diseases, from different angles. However, in order to get a systematic view on disease it is necessary to combine these heterogeneous data. The large amounts of diverse data requires building machine learning models that can help, e.g. to identify which genes are related to disease. Additionally, there is a need to compose reliable integrated data sets that researchers could effectively work with. In this thesis we demonstrate how to combine and analyze different types of biological data in the example of three biological domains: Alzheimer’s disease, immunology, and toxicology. More specifically, we combine data sets related to Alzheimer’s disease into a novel heterogeneous network-based data set for Alzheimer’s disease (HENA). We then apply graph convolutional networks, state-of-the-art deep learning methods, to node classification task in HENA to find genes that are potentially associated with the disease. Combining patient’s data related to immune disease helps to uncover its pathological mechanisms and to find better treatments in the future. We analyse laboratory data from patients’ skin and blood samples by combining them with clinical information. Subsequently, we bring together the results of individual analyses using available domain knowledge to form a more systematic view on the disease pathogenesis. Toxicity testing is the process of defining harmful effects of the substances for the living organisms. One of its applications is safety assessment of drugs or other chemicals for a human organism. In this work we identify groups of toxicants that have similar mechanism of actions. Additionally, we develop a classification model that allows to assess toxic actions of unknown compounds.https://www.ester.ee/record=b523255

Approaches, applications, and challenges in physiological emotion recognition — a tutorial overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions