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Background: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. Methods: 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. Results: The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. Conclusion: The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power. [ABSTRACT FROM AUTHOR]

Motivation: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements. [ABSTRACT FROM AUTHOR]

Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process. [ABSTRACT FROM AUTHOR]

Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient's symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat. [ABSTRACT FROM AUTHOR]

With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars. [ABSTRACT FROM AUTHOR]

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region's economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera's view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ("presence/absence" detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating. [ABSTRACT FROM AUTHOR]

Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain. [ABSTRACT FROM AUTHOR]

The goal of semantic segmentation is to segment the input image according to semantic information and predict the semantic category of each pixel from a given label set. With the gradual intellectualization of modern life, more and more applications need to infer relevant semantic information from images for subsequent processing, such as augmented reality, autonomous driving, video surveillance, etc. This paper reviews the state-of-the-art technologies of semantic segmentation based on deep learning. Because semantic segmentation requires a large number of pixel-level annotations, in order to reduce the fine-grained requirements of annotation and reduce the economic and time cost of manual annotation, this paper studies the works on weakly-supervised semantic segmentation. In order to enhance the generalization ability and robustness of the segmentation model, this paper investigates the works on domain adaptation in semantic segmentation. Many types of sensors are usually equipped in some practical applications, such as autonomous driving and medical image analysis. In order to mine the association between multi-modal data and improve the accuracy of the segmentation model, this paper investigates the works based on multi-modal data fusion semantic segmentation. The real-time performance of the model needs to be considered in practical application. This paper analyzes the key factors affecting the real-time performance of the segmentation model and investigates the works on real-time semantic segmentation. Finally, this paper summarizes the challenges and promising research directions of semantic segmentation tasks based on deep learning. [ABSTRACT FROM AUTHOR]

The advent of deep learning has brought great change to the community of computer science and also revitalized numerous fields where traditional machine learning methods failed to make breakthroughs. Benefitted from the development of deep learning, analysis of medical images, which used to be a challenging yet exhausting task carried out manually by physicians, has experienced fast development as well. However, training deep learning models in these systems for analysis from scratch can be quite challenging. The small-scale data can't guarantee the performance of the developed systems, while large-scale data is usually unavailable due to expensive costs in the process of collection and storage. To allow a fast transition from one domain to another for reuse, experts and researchers have extensively delved transfer learning, which turns out to be an efficient and low-cost learning technique. In this paper, we will present a comprehensive survey of transfer learning on medical image analysis. The imaging modalities include but not limits to Computed Tomography (CT), Ultrasound (US), and Magnetic Resonance Imaging (MRI). The subjects covered in this paper include the brain, breast, lung, kidney, etc. Besides, this survey provides systematic knowledge about deep learning and transfer learning for beginners. Readers with different backgrounds can easily catch up with the interdisciplinary knowledge and new trends of transfer learning via this survey. [ABSTRACT FROM AUTHOR]

Coronary artery disease is among one of the diseases human suffer most. Intravascular coronary arterial image analysis consists of denoising, segmentation, detection, and three-dimensional reconstruction, having a significant meaning for auxiliary diagnosis and treatment of coronary artery disease. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) are the two most commonly applied intravascular coronary arterial imaging techniques. Based on these fundamental imaging techniques, in recent years, many advanced technologies from traditional machine learning algorithms to deep learning methods were employed in the analysis of intravascular coronary arterial images and made huge progress in this field. In this survey, we reviewed more than one hundred papers published in top journals or conferences such as Neural Networks and MICCAI. These papers proposed approaches or schemes for the intravascular coronary arterial image analysis, including lumen border segmentation, atherosclerotic plaque characterization, media-adventitia segmentation, stent strut detection, and three-dimensional reconstruction. Our survey began with introducing coronary artery intravascular imaging techniques, essential neural networks, and deep learning and then presented an across-the-board review of methods, applications, and trends of intravascular image analysis. This survey is more comprehensive than other articles not only for its scope and reference number but also for discussing the future direction in this field. Compared to other review papers in this field, this article could assist beginners in constructing a basic knowledge frame of coronary artery intravascular image analysis methods and brought state-of-the-art progress in this field to fellow researchers. We hope this paper could benefit either the beginners for coronary arterial image analysis or experienced researchers. [ABSTRACT FROM AUTHOR]

Retinal optical coherence tomography (OCT) images provide fundamental information regarding the health of the posterior eye (e.g., the retina and choroid). Thus, the development of automatic image analysis methods (e.g., segmentation) is fundamental to provide clinicians and researchers with quantitative data that facilitates decision making. In recent years, various machine learning (ML) methods have been developed to perform these automated image analyses, improving performance over traditional methods, increasing repeatability, and reducing the use of time-consuming manual analysis. Deep learning (DL), a sub-field of ML, represents a new approach that can improve image processing outcomes. Results published to date demonstrate that DL architectures generally achieve superior performance to previously proposed methods based on traditional image analysis or early ML techniques. Thus, DL methods for OCT image analysis have provided an important advance in the area of retinal layer segmentation in images from healthy eyes as well as those with pathologies. This paper provides a comprehensive narrative literature review of current DL layer segmentation methods applied to OCT images of the posterior segment of the eye. The manuscript provides an overview on the state-of-the-art in deep learning as well as highlighting some important areas for future developments to extend the analysis methods in this field. [ABSTRACT FROM AUTHOR]

• We review artificial intelligence applications of brain image preprocessing. • We investigate several commonly seen brain diseases and survey their intelligence-based methods. • We discuss AI's future in brain disease studies. Brain diseases threaten hundreds of thousands of people over the world. Medical imaging techniques such as MRI and CT are employed for various brain disease studies. As artificial intelligence succeeded in image analysis, scientists employed artificial intelligence, especially deep learning technologies, to assist brain disease studies. The AI applications for brain disease studies can be divided into two categories. The first category is preprocessing, including denoising, registration, skull-stripping, intensity normalization, and data augmentation. The second category is the clinical application that contains lesion segmentation, disease detection, grade classification, and outcome prediction. In this survey, we reviewed over one hundred representative papers on how to apply AI to brain disease studies. We first introduced AI-based preprocessing for brain disease studies. Second, we reviewed the influential works of AI-based brain disease studies. At last, we also discussed three development trends in the future. We hope this survey will inspire both expert-level researchers and entry-level beginners. [ABSTRACT FROM AUTHOR]

Early stage smoke detection using image and video analysis is an important area of research due to its enormous applications in mitigating fire hazards and ensuring environmental safety. Numerous solutions have been proposed for real-time smoke detection using conventional image processing, machine learning, and deep learning techniques. Smoke pattern, motion analysis, color and texture are important characteristics that help identify it in the outdoor environment. Vision-based Smoke detection algorithms can be broadly classified into three categories: smoke classification, segmentation, and bounding box estimation. This paper presents a comprehensive survey of existing techniques on smoke detection in the outdoor environment using image and video analysis. To perform the survey, initially 271 articles were collected from different sources like Google Scholar, Science Direct, IEEE Xplore, SpringerLink, Wiley and ACM Digital Library using the keyword search. Based on their focus on the vision-based solutions for the outdoor environment, 126 articles were identified as relevant to the present survey. Starting from the initial IP approaches that are frequently referred in the literature, machine learning and deep learning approaches have also been reviewed for each type of smoke detection. Performance of algorithms, datasets used in the research, evaluation metrics, challenges and future directions of research are also discussed. [ABSTRACT FROM AUTHOR]

Having in mind the demographic explosion and the pressing need to ensure food supplies for the entire world, this review came with a synthetic presentation of the most modern non-invasive technique of investigation used in meat quality evaluation for livestock that has evolved and developed in the last 60 years. These modern techniques such as ultrasound, Computer Tomography (CT) and Video Image Analyses (VIA) were first introduced in human medicine and afterwards were adopted by animal research and implemented gradually as a tool to predict meat at an early age. Ultrasound, CT and VIA developed very quickly and helped considerably the meat industry focusing on assurance of human health. These techniques became popular as they were able to accurately predict meat quality in vivo avoiding the need to slaughter the animals, which assisted in the evaluation of meat quality in breeding candidates. One of the evaluation methods presented above was adopted by Romania for the first -time to improve meat quality evaluation in sheep, and as a result some disadvantages regarding the classical procedure for evaluating sheep for meat production have been removed. The classical procedure for sheep meat evaluation used in Romania until 2012 was to slaughter the animal in order to obtain meat carcass measurements (commercial yield, slaughtering yield, dressing percentage and meat to bone ratio), a method which involves hard and expensive work and sacrificing the animal, which otherwise may have been used in reproduction. The ultrasound method was chosen as an option in sheep meat quality evaluation thanks to its efficiency with providing fast and accurate results, particularly since non-invasive ultrasound measurements very well correlated with meat traits. Ultrasound parameters were measured in vivo in two points on the Longissimus Dorsi muscle, which in Teleorman Black Head sheep is a muscle very well correlated with carcass meat amount. Very soon the ultrasound evaluation was used in goats of the Carpatina breed with similar ultrasound parameters recognized for meat quality. Consequently, the use of ultrasound measurements in genetic evaluation systems has been recommended to increase the rate of genetic improvement for carcass yield and quality traits. That is why the ultrasound method is recommended in meat quality evaluation to facilitate classification of animals specialized for meat quality. Animal breeders and farmers use ultrasound in vivo evaluation to maximize genetic gain for meat traits by selecting the best individuals. The application of this non-invasive technique provides prospects for livestock breeders and the meat industry for an accurate in vivo meat evaluation for quality carcass classification, very important in maximizing economically important traits. [ABSTRACT FROM AUTHOR]

Numerous in vitro biofilm model systems are available to study oral biofilms. Over the past several decades, increased understanding of oral biology and advances in technology have facilitated more accurate simulation of intraoral conditions and have allowed for the increased generalizability of in vitro oral biofilm studies. The integration of contemporary systems with confocal microscopy and 16S rRNA community profiling has enhanced the capabilities of in vitro biofilm model systems to quantify biofilm architecture and analyse microbial community composition. In this review, we describe several model systems relevant to modern in vitro oral biofilm studies: the constant depth film fermenter, Sorbarod perfusion system, drip–flow reactor, modified Robbins device, flowcells and microfluidic systems. We highlight how combining these systems with confocal microscopy and community composition analysis tools aids exploration of oral biofilm development under different conditions and in response to antimicrobial/anti‐biofilm agents. The review closes with a discussion of future directions for the field of in vitro oral biofilm imaging and analysis. [ABSTRACT FROM AUTHOR]