An Automated Image Analysis and Cell Identification System Using Machine Learning Methods

2020 ◽  
Author(s):  
Keiko Itano ◽  
Koji Ochiai ◽  
Koichi Takahashi ◽  
Takahide Matsushima ◽  
Hiroshi Asahara
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Automated Image Analysis ◽  
Identification System ◽  
Learning Methods ◽  
Fluorescent Cell ◽  
Machine Learning Methods ◽  
A Cell ◽  
System Cell ◽  
Phenotype Identification

Abstract In many biological laboratories, biologists analyze images and identify cell or organ states manually. There are some problems: lack of human resource and high experimental costs, among others. Identification results vary according to the person. To solve these problems, the process automation of biologists’ operations and quantitative identification are needed. Here, a cell-foci-phenotype identification system is developed by applying image processing and machine learning methods to fluorescent cell images. With this system, cell-foci-phenotype with high accuracy can be predicted and biologists’ efforts in doing image analysis can be reduced.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

scholarly journals Brain signal and image analysis using machine learning methods

2017 ◽  
Author(s):  
Βασίλειος Κανάς
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

Η δουλειά η οποία παρουσιάζεται σε αυτήν την διδακτορική διατριβή ανήκει στο πλαίσιο της μηχανικής μάθησης και την ανάπτυξη μεθοδολογιών για την επεξεργασία και ανάλυση μονοδιάστατων και διδιάστατων εγκεφαλικών σημάτων. Πιο συγκεκριμένα, εστιάζεται: 1) στην μελέτη, επεξεργασία και ανάλυση εγκεφαλικών σημάτων ηλεκτροκορτικογραφήματος για τον εντοπισμό φωνητικής δραστηριοποίησης και την ταξινόμηση συλλαβών με σκοπό τον σχεδιασμό και ανάπτυξη ενός BCI συστήματος για την αποκατάσταση ασθενών με προβλήματα ομιλίας, 2) στην επεξεργασία εικόνων μαγνητικής τομογραφίας εγκεφάλου με σκοπό την τμηματοποίηση και ταξινόμηση καρκινικών εγκεφαλικών όγκων, και 3) την μαθηματική μοντελοποίηση δικτύων βιολογικών νευρώνων.

scholarly journals On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods

Sensors ◽  
2014 ◽  
Vol 14 (7) ◽  
pp. 12191-12206 ◽  
Author(s):  
Kyosuke Yamamoto ◽  
Wei Guo ◽  
Yosuke Yoshioka ◽  
Seishi Ninomiya
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Learning Methods ◽  
Tomato Fruits ◽  
Machine Learning Methods

scholarly journals Resilience of clinical text de-identified with “hiding in plain sight” to hostile reidentification attacks by human readers

2020 ◽  
Vol 27 (9) ◽  
pp. 1374-1382
Author(s):  
David S Carrell ◽  
Bradley A Malin ◽  
David J Cronkite ◽  
John S Aberdeen ◽  
Cheryl Clark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Identification System ◽  
Learning Methods ◽  
Clinical Text ◽  
Secondary Use ◽  
Machine Learning Methods ◽  
Healthcare Settings ◽  
Attack Scenario ◽  
Novel Method ◽  
Personally Identifying Information

Abstract Objective Effective, scalable de-identification of personally identifying information (PII) for information-rich clinical text is critical to support secondary use, but no method is 100% effective. The hiding-in-plain-sight (HIPS) approach attempts to solve this “residual PII problem.” HIPS replaces PII tagged by a de-identification system with realistic but fictitious (resynthesized) content, making it harder to detect remaining unredacted PII. Materials and Methods Using 2000 representative clinical documents from 2 healthcare settings (4000 total), we used a novel method to generate 2 de-identified 100-document corpora (200 documents total) in which PII tagged by a typical automated machine-learned tagger was replaced by HIPS-resynthesized content. Four readers conducted aggressive reidentification attacks to isolate leaked PII: 2 readers from within the originating institution and 2 external readers. Results Overall, mean recall of leaked PII was 26.8% and mean precision was 37.2%. Mean recall was 9% (mean precision = 37%) for patient ages, 32% (mean precision = 26%) for dates, 25% (mean precision = 37%) for doctor names, 45% (mean precision = 55%) for organization names, and 23% (mean precision = 57%) for patient names. Recall was 32% (precision = 40%) for internal and 22% (precision =33%) for external readers. Discussion and Conclusions Approximately 70% of leaked PII “hiding” in a corpus de-identified with HIPS resynthesis is resilient to detection by human readers in a realistic, aggressive reidentification attack scenario—more than double the rate reported in previous studies but less than the rate reported for an attack assisted by machine learning methods.

scholarly journals Machine Learning Methods for Histopathological Image Analysis: A Review

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 562
Author(s):  
Jonathan de Matos ◽  
Steve Ataky ◽  
Alceu de Souza Britto ◽  
Luiz Soares de Oliveira ◽  
Alessandro Lameiras Koerich
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Cancer Diagnosis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Private Datasets ◽  
Aided Diagnosis ◽  
Histopathological Image Analysis

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

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