Deep learning for image-based cancer detection and diagnosis − A survey

Hu, Zhiying; Tang, Jinshan; Wang, Ziming; Zhang, Kai; Zhang, Ling; Sun, Qingling

doi:10.1016/j.patcog.2018.05.014

Cited by 416 publications

(206 citation statements)

References 62 publications

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“…The challenges inherent in image-based diagnosis and decision-making are not unique to reproductive medicine. Efforts directed at improving accuracy and standardization of image analysis through development of computer-aided tools have recently gained attention in other medical fields [3][4][5] , including dermatology and oncology, where deep learning techniques and architectures are in current use for image analysis and to assist diagnosis 6,7 .…”

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confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model's generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.

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confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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“…The individual research results provide sufficient information and insight into the applications of deep learning for detecting, classifying, segmenting and diagnosing various diseases and abnormalities in specific anatomical regions of interest (ROI). Without a doubt deep learning application in the medical field will further develop as it has already achieved remarkable results in medical image analysis [66], and more precisely, in image-based cancer detection and diagnosis [67]. This may increase the efficiency and quality of healthcare in the long-run, thus reducing the risk of late-diagnosis of serious diseases.…”

Section: Discussing the Resultsmentioning

confidence: 99%

Deep Learning and Medical Diagnosis: A Review of Literature

Bakator

Radosav

2018

MTI

398

159

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Abstract:In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

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“…It was assumed that CNN was appropriate for extracting the features of fault behavior, because of its high accuracy. CNN has delivered high performance in other fields such as cancer detection (Hu et al (2018); Esteva et al (2018)). Therefore, we assumed that CNN could learn and diagnose the imaged behavior of the heat source system.…”

Section: Structure Of Convolutional Neural Networkmentioning

confidence: 99%

Fault detection and diagnosis for heat source system using convolutional neural network with imaged faulty behavior data

Miyata

Lim

Akashi

et al. 2019

Science and Technology for the Built Environment

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Faults that impair performance can occur in a heat source system because it comprises various devices and has complex controls. This article presents a novel method for fault detection and diagnosis (FDD). This study focused on a real system with a water thermal storage tank. First, system behaviors in response to faults were determined using a detailed system simulation. Then, a fault database was generated using the simulation results with fault labels. We preprocessed the database and converted the data into images. Then, convolutional neural networks (CNNs) were trained using the database, and the trained CNNs were used for diagnosing real data. The accuracy of the CNNs was 98.7% in training, and real data were diagnosed with probabilities. We analyzed the real data, where the probability indicated the likely presence of a fault and reviewed how the real data were similar to the fault assumed in the simulation. We concluded that the proposed FDD method will help in analyzing real data, as it indicates faults emerging in the real data with probability, whereas conventional data analysis requires checking the data using expert knowledge.

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Deep learning for image-based cancer detection and diagnosis − A survey

Cited by 416 publications

References 62 publications

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Deep Learning and Medical Diagnosis: A Review of Literature

Fault detection and diagnosis for heat source system using convolutional neural network with imaged faulty behavior data

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Deep learning for image-based cancer detection and diagnosis − A survey

Cited by 416 publications

References 62 publications

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Deep Learning and Medical Diagnosis: A Review of Literature

Fault detection and diagnosis for heat source system using convolutional neural network with imaged faulty behavior data

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Deep learning for image-based cancer detection and diagnosis − A survey