Detecting Keratoconus From Corneal Imaging Data Using Machine Learning

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Diagnostics ◽

10.3390/diagnostics11050742 ◽

2021 ◽

Vol 11 (5) ◽

pp. 742

Author(s):

Rima Hajjo ◽

Dima A. Sabbah ◽

Sanaa K. Bardaweel ◽

Alexander Tropsha

Keyword(s):

Machine Learning ◽

High Specificity ◽

Cancer Biomarkers ◽

Cancer Prognosis ◽

Current Status ◽

Imaging Data ◽

Non Invasive ◽

Machine Learning Methods ◽

Cancer Types ◽

Magnetic Resonance Imaging Mri

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213018500112 ◽

2018 ◽

Vol 27 (03) ◽

pp. 1850011 ◽

Cited By ~ 5

Author(s):

Athanasios Tagaris ◽

Dimitrios Kollias ◽

Andreas Stafylopatis ◽

Georgios Tagaris ◽

Stefanos Kollias

Keyword(s):

Machine Learning ◽

Neurodegenerative Disorder ◽

Developed Countries ◽

Machine Learning Techniques ◽

Network Architectures ◽

Imaging Data ◽

Learning Techniques ◽

Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

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Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data

Journal of Neuroimaging ◽

10.1111/jon.12214 ◽

2015 ◽

Vol 25 (5) ◽

pp. 738-747 ◽

Cited By ~ 46

Author(s):

Martin Dyrba ◽

Frederik Barkhof ◽

Andreas Fellgiebel ◽

Massimo Filippi ◽

Lucrezia Hausner ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Cognitive Impairment ◽

Diffusion Tensor ◽

Imaging Data ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Data ◽

Machine Learning Classification ◽

Prodromal Alzheimer’S Disease

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Machine learning-assisted imaging analysis of a human epiblast model

Integrative Biology ◽

10.1093/intbio/zyab014 ◽

2021 ◽

Author(s):

Agnes M Resto Irizarry ◽

Sajedeh Nasr Esfahani ◽

Yi Zheng ◽

Robin Zhexuan Yan ◽

Patrick Kinnunen ◽

...

Keyword(s):

Machine Learning ◽

Stem Cells ◽

Cell Tracking ◽

Complex Structure ◽

Cyst Formation ◽

System Level ◽

Imaging Data ◽

Cell Level ◽

Human Blastocyst ◽

Cell Cell

Abstract The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell–cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell–cell and cell–environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.

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PyHIST: A Histological Image Segmentation Tool

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008349 ◽

2020 ◽

Vol 16 (10) ◽

pp. e1008349

Author(s):

Manuel Muñoz-Aguirre ◽

Vasilis F. Ntasis ◽

Santiago Rojas ◽

Roderic Guigó

Keyword(s):

Machine Learning ◽

Input Image ◽

Biological Knowledge ◽

Imaging Data ◽

Tissue Segmentation ◽

Histological Image ◽

Command Line Tool ◽

Machine Learning Applications ◽

Histopathological Images ◽

High Resolution Images

The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool aimed at tile generation for machine learning applications. From a given input image, the PyHIST pipeline i) optionally rescales the image to a different resolution, ii) produces a mask for the input image which separates the background from the tissue, and iii) generates individual image tiles with tissue content.

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Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa501 ◽

2020 ◽

Vol 493 (3) ◽

pp. 4209-4228 ◽

Cited By ~ 6

Author(s):

Ting-Yun Cheng ◽

Christopher J Conselice ◽

Alfonso Aragón-Salamanca ◽

Nan Li ◽

Asa F L Bluck ◽

...

Keyword(s):

Machine Learning ◽

Imaging Data ◽

Morphological Classification ◽

Learning Methods ◽

Visual Classification ◽

Machine Learning Methods ◽

Dark Energy Survey ◽

Galaxy Classification ◽

Energy Survey

ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

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Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-011420-031537 ◽

2020 ◽

Vol 3 (1) ◽

pp. 61-87 ◽

Cited By ~ 10

Author(s):

Theodore Alexandrov

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Mass Spectrometry ◽

Image Analysis ◽

Imaging Mass Spectrometry ◽

Rapid Evolution ◽

Life Sciences ◽

Imaging Data ◽

Enabling Technology ◽

Tissue Sections

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology—imaging mass spectrometry—generate big hyperspectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

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Machine Classification of Transient Images

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314013842 ◽

2014 ◽

Vol 10 (S306) ◽

pp. 288-291

Author(s):

Lise du Buisson ◽

Navin Sivanandam ◽

Bruce A. Bassett ◽

Mathew Smith

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Machine Learning Techniques ◽

Imaging Data ◽

Transient Detection ◽

Minimum Error ◽

Learning Techniques ◽

Nearest Neighbours ◽

Transient Imaging

AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

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