Application of Models based on Human Vision in Medical Image Processing: A Review Article

Nikroorezaei, Farzaneh; Esmaili, Somayeh Saraf

doi:10.5815/ijigsp.2019.12.03

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The application of self-supervised learning methods in the medical field has achieved great success, and obtaining good performance that exceeds the supervised pre-training model on certain medical image tasks. However, directly applying existing self-supervised learning methods is not necessarily suitable for medical image tasks [57].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

Xu¹

2021

IJIGSP

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In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets. To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively review the application of self-supervised learning in the medical field. This article provides the latest and most detailed overview of self-supervised learning in the medical field and promotes the development of unsupervised learning in the field of medical imaging. These methods are divided into three categories: context-based, generation-based, and contrast-based, and then show the pros and cons of each category and evaluates their performance in downstream tasks. Finally, we conclude with the limitations of the current methods and discussed the future direction.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

Xu¹

2021

IJIGSP

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“…It is worth exploring more powerful semi-supervised and self-supervised learning methods so that the novel methods may prove to be as promising as supervised models in the future. Self-supervised methods have gained popularity in the medical imaging field due to their ability to outperform supervised approaches on specific tasks, as reported in previous studies [82]. However, when implementing self-supervised frameworks in the medical domain, it is essential to address the challenge of data imbalance [83].…”

Section: Future Directionsmentioning

confidence: 99%

Active Semi-Supervised Learning via Bayesian Experimental Design for Lung Cancer Classification Using Low Dose Computed Tomography Scans

et al. 2023

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We introduce an active, semisupervised algorithm that utilizes Bayesian experimental design to address the shortage of annotated images required to train and validate Artificial Intelligence (AI) models for lung cancer screening with computed tomography (CT) scans. Our approach incorporates active learning with semisupervised expectation maximization to emulate the human in the loop for additional ground truth labels to train, evaluate, and update the neural network models. Bayesian experimental design is used to intelligently identify which unlabeled samples need ground truth labels to enhance the model’s performance. We evaluate the proposed Active Semi-supervised Expectation Maximization for Computer aided diagnosis (CAD) tasks (ASEM-CAD) using three public CT scans datasets: the National Lung Screening Trial (NLST), the Lung Image Database Consortium (LIDC), and Kaggle Data Science Bowl 2017 for lung cancer classification using CT scans. ASEM-CAD can accurately classify suspicious lung nodules and lung cancer cases with an area under the curve (AUC) of 0.94 (Kaggle), 0.95 (NLST), and 0.88 (LIDC) with significantly fewer labeled images compared to a fully supervised model. This study addresses one of the significant challenges in early lung cancer screenings using low-dose computed tomography (LDCT) scans and is a valuable contribution towards the development and validation of deep learning algorithms for lung cancer screening and other diagnostic radiology examinations.

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“…These methods struggle to model the compounding factors of diseases and the comorbidity of multiple diseases [14]. Conversely, multi-label classifiers can effectively represent the co-occurrence of diseases by assigning subjects to two or more labels [15]. In addition, traditional computer vision methods used in medical image processing are not as powerful as modern deep learning approaches for representation learning [16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks

Endah H,

Wijaya,

Khotibul Ahsan

2023

J. Soft Comput. Explor.

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Cirrhosis, a prevalent and life-threatening liver condition, demands early detection for effective intervention. This study investigates the potential of machine learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in cirrhosis prediction using a dataset from Kaggle containing 418 observations and 20 attributes. Performance evaluation involves metrics like accuracy, precision, recall, and F1-score, revealing CNN's superior performance with an 84% accuracy rate. The study highlights the importance of algorithm selection and feature engineering in medical diagnosis. Moreover, a comparison with traditional machine learning techniques underscores CNN's prowess in this domain. Beyond cirrhosis, CNNs offer promise for automating feature extraction from medical imagery and recognizing complex patterns, potentially transforming diagnostic accuracy in healthcare.

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Application of Models based on Human Vision in Medical Image Processing: A Review Article

Cited by 4 publications

References 16 publications

A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

Active Semi-Supervised Learning via Bayesian Experimental Design for Lung Cancer Classification Using Low Dose Computed Tomography Scans

Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks

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