AI-Powered Image Security:Utilizing Autoencoders for Advanced Medical Image Encryption

With the rapid advancement in artificial intelligence(AI)and its application in the Internet of Things(IoT),intelligent technologies are being introduced in the medical field,giving rise to smart healthcare systems.The medical imaging data contains sensitive information,which can easily be stolen or tampered with,necessitating secure encryption schemes designed specifically to protect these images.This paper introduces an artificial intelligence-driven novel encryption scheme tailored for the secure transmission and storage of high-resolution medical images.The proposed scheme utilizes an artificial intelligence-based autoencoder to compress highresolution medical images and to facilitate fast encryption and decryption.The proposed autoencoder retains important diagnostic information even after reducing the image dimensions.The low-resolution images then undergo a four-stage encryption process.The first two encryption stages involve permutation and the next two stages involve confusion.The first two stages ensure the disruption of the structure of the image,making it secure against statistical attacks.Whereas the two stages of confusion ensure the effective concealment of the pixel values making it difficult to decrypt without secret keys.This encrypted image is then safe for storage or transmission.The proposed scheme has been extensively evaluated against various attacks and statistical security parameters confirming its effectiveness in securing medical image data.

The Artificial Intelligence-Enabled Medical Imaging:Today and Its Future

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.

Collaborations of Industry,Academia,Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China

In recent years,artificial intelligence (AI) has developed rapidly in the field of medical imaging.However,the collaborations among hospitals,research institutes and enterprises are insufficient at the present,and there are various issues in technological transformation and value landing of products in this area.To solve the core problems in the developmental path of medical imaging AI,the Chinese Innovative Alliance of Industry,Education,Research and Application of Artificial Intelligence for Medical Imaging compiled the White Paper on Medical Image AI in China.This article introduces the current status of collaboration,the clinical demands for medical imaging AI technique,and the key points in AI technology transformation:robustness,usability and security.We are facing challenges of lacking industry standards,data desensitization standard,assessment system,as well as corresponding regulations and policies to realize the application values of AI products in medical imaging.Further development of AI in medical imaging requires breakthroughs of the core algorithm,deep involvement of doctors,input from capitals,patience from societies,and most importantly,the resolutions from government for multiple difficulties in links of landing the technology.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Releasing of The White Paper on Medical Imaging Artificial Intelligence in China

IN the afternoon of March 26,2019,The White Paper on Medical Imaging Artificial Intelligence in China (hereinafter referred to as the “white paper”) was officially released in Beijing by the Chinese Innovative Alliance of Industry,Education,Research and Application of Artificial Intelligence for Medical Imaging (CAIERA)(Figure 1).The white paper was co-operatively written by the medical imaging experts from the tertiary Chinese hospitals,the scientific experts from AI research institutions and the leading AI medical enterprises in China.The contents of the white paper not only cover the uptodate application of AI in medical field,the latest advances of AI algorithms in medical image processing,the data requirement for medical AI development,and the current situation of structured data,but also expatiate the goal and challenge of clinical application for medical imaging AI development in 16 medical subject areas,which helps to identify the demands and opportunities for the AI industry.

Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights

Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

A review of medical artificial intelligence

Since the concept of“artificial intelligence”was introduced in 1956,it has led to numerous technological innovations in human medicine and completely changed the traditional model of medicine.In this study,we mainly explain the application of artificial intelligence in various fields of medicine from four aspects:machine learning,intelligent robot,image recognition technology,and expert system.In addition,we discuss the existing problems and future trends in these areas.In recent years,through the development of globalization,various research institutions around the world has conducted a number of researches on this subject.Therefore,medical artificial intelligence has attained significant breakthroughs and will demonstrate wide development prospection in the future.

Current trends of artificial intelligence in cancer imaging

In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clinical and imaging cancer workflow.The growing amounts of available“big data”coupled to the increasing computational power have enabled the development of computerbased systems capable to perform advanced tasks in many areas of clinical care,especially in medical imaging.ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions.DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks;convolutional neural networks are the most common DL models used in medical image analysis.AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications.In detail,we explored the emerging applications of AI in oncological risk assessment,lesion detection,characterization,staging,and therapy response.Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice.Nevertheless,AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent years.Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing power.Deep learning models are at the forefront of this advancement.However,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their conclusions.Such a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep learningmodels.The situation is substantially different in safety-critical applications.The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case.Significantly,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an option.This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment industries.Explainability is the term introduced in the preceding years.The AImodel’s black box nature will become explainable with these frameworks.Especially in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial use.These models’explainable natures will help them commercially in diagnosis decisions in the medical field.This paper explores the different frameworks for the explainability of AI models in the medical field.The available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Colorectal cancer(CRC)was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants.It can be prevented if glandular tissue(adenomatous polyps)is detected early.Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps.However,it has some limitations that include the high polyp miss rate for smaller(<10 mm)or flat polyps,which are easily missed during visual inspection.Due to the rapid advancement of technology,artificial intelligence(AI)has been a thriving area in different fields,including medicine.Particularly,in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC.This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps,as well as an insightful analysis of the main advantages and misconceptions in the field.

Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Artificial intelligence(AI)has seen tremendous growth over the past decade and stands to disrupts the medical industry.In medicine,this has been applied in medical imaging and other digitised medical disciplines,but in more traditional fields like medical physics,the adoption of AI is still at an early stage.Though AI is anticipated to be better than human in certain tasks,with the rapid growth of AI,there is increasing concerns for its usage.The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy.Topics on AI for image acquisition,image segmentation,treatment delivery,quality assurance and outcome prediction will be explored as well as the interaction between human and AI.This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.

Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

BACKGROUND Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.AIM To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.METHODS The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs.181 radiographs were utilized for training(161)and validating(20)a U-Net neural network to achieve a mean Sørensen–Dice index>97%on bone segmentation.84 test radiographs were used for manual(computer assisted)and automated measurements of hallux valgus severity determined by hallux valgus(HVA)and intermetatarsal angles(IMA).The reliability of manual and computerbased measurements was calculated using the interclass correlation coefficient(ICC)and standard error of measurement(SEM).Inter-and intraobserver reliability coefficients were also compared.An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.RESULTS Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians.For HVA,the ICC between manual measurements was 0.96-0.99.For IMA,ICC was 0.78-0.95.Comparing manual against automated computer measurement,the reliability was high as well.For HVA,absolute agreement ICC and consistency ICC were 0.97,and SEM was 0.32.For IMA,absolute agreement ICC was 0.75,consistency ICC was 0.89,and SEM was 0.21.Additionally,a strong correlation(0.80)was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus,according to an operative treatment algorithm proposed by EFORT.CONCLUSION The proposed automated,artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool,with comparable accuracy to manual measurements by expert clinicians.Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs,classify the deformity severity,streamline preoperative decision-making prior to corrective surgery.

Preliminary landscape analysis of deep tomographic imaging patents

Over recent years,the importance of the patent literature has become increasingly more recognized in the aca-demic setting.In the context of artificial intelligence,deep learning,and data sciences,patents are relevant to not only industry but also academe and other communities.In this article,we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature.Our search tool is PatSeer.Our patent biblio-metric data is summarized in various figures and tables.In particular,we qualitatively analyze key deep tomographic patent literature.

Vision transformer architecture and applications in digital health:a tutorial and survey

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges.

Deep Fakes in Healthcare:How Deep Learning Can Help to Detect Forgeries

With the increasing use of deep learning technology,there is a growing concern over creating deep fake images and videos that can potentially be used for fraud.In healthcare,manipulating medical images could lead to misdiagnosis and potentially life-threatening consequences.Therefore,the primary purpose of this study is to explore the use of deep learning algorithms to detect deep fake images by solving the problem of recognizing the handling of samples of cancer and other diseases.Therefore,this research proposes a framework that leverages state-of-the-art deep convolutional neural networks(CNN)and a large dataset of authentic and deep fake medical images to train a model capable of distinguishing between authentic and fake medical images.Specifically,the paper trained six CNN models,namely,ResNet101,ResNet50,DensNet121,DenseNet201,MobileNetV2,andMobileNet.These models had trained using 2000 samples over three classes:Untampered,False-Benign,and False-Malicious,and compared against several state-of-the-art deep fake detection models.The proposed model enhanced ResNet101 by adding more layers,achieving a training accuracy of 99%.The findings of this study show near-perfect accuracy in detecting instances of tumor injections and removals.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Classification and detection of dental images using meta-learning

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input.Instead of just memorizing a task,this is accomplished through teaching a model how to learn.Algorithms for meta-learning are typically trained on a collection of training problems,each of which has a limited number of labelled instances.Multiple Xray classification tasks,including the detection of pneumonia,coronavirus disease 2019,and other disorders,have demonstrated the effectiveness of meta-learning.Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods.Due to the high cost and lengthy collection process associated with dental imaging datasets,this is significant for dental X-ray classification jobs.The ability to train models that are more resistant to fresh input is another benefit of meta-learning.