Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques

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Senaras et al. Studies have proposed the generation of synthetic patches for augmentation of pathology data sets 76 , 77 in order to compensate for the lack of labelled data, in particular for Gleason grade 5 tumours. GAN, generative adversarial network. Automatic classification of digitized slides has the potential to improve diagnostic workflow in pathology laboratories, to address the shortage of pathologists in different regions of the world, to provide quality assurance reviews and second opinions to smaller centres and to potentially improve consensus in reporting.

In summary, published data indicate the tremendous potential of both feature-based and DL-based classification techniques The data being provided to neural networks must be accurate, not just adequate, so that the system does not learn from misinformation arising from technical issues such as tissue sampling, specimen preparation, slide staining, imaging variability and intrapatch tissue heterogeneity.

The widespread use of digital pathology has the potential to positively enhance health-care delivery by enabling the use of customized precision-care pathways, but only once the system is optimized to achieve appropriate and acceptable regionally defined sensitivity and specificity values. Management of individual patients with prostate cancer is often challenging owing to the biological diversity between patients and the fact that histopathology alone cannot accurately predict prostate cancer outcomes Over the past decade, the addition of molecular profiling and genomic-based risk prediction models has been met with increasing enthusiasm and interest ML methods are used in such genomics studies to identify, within data-rich gene expression profiles, the genes or groups of genes for which expression specificity to predict a certain clinical outcome is high This information could then be used for developing diagnostic and risk stratification tools, for determining optimal individualized treatment and for producing targeted drug regimens.

A number of ML-based genomic classifiers are commercially available.


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Decipher 80 uses a random forest algorithm for prediction of prostate cancer metastasis. On the basis of a cohort of 1, patients, the AUC for Decipher was 0. Nguyen et al. Lee et al.

Top 10 Applications of Machine Learning in Healthcare

Applying patient and dose information without ML was not successful in predicting the late toxicity. In , Lee et al. They developed a sophisticated and powerful computational fusion method to interrogate the integration of features derived from quantitative histopathology, such as glandular morphology, architecture, orientation and texture, with protein expression levels from 40 radical prostatectomy specimens. This approach was able to predict biochemical recurrence with a mean AUC of 0. In the coming years, ML methods operating on fused data streams will require considerable computational power for analysis in clinically relevant applications but will hopefully increase the receiver operating characteristics for predicting individual patient outcomes and for selecting personalized care regimens 84 , The current sparsity of publications that report the use of fused data for patient stratification might also be due to the challenges that are still associated with the study of genomics of prostate cancer Human beings are incredibly slow, inaccurate, and brilliant.

This powerful combination is already altering the dynamics of medicine, including urological imaging and pathology, and industry is investing heavily in AI applications. The AI field is advancing at a staggering rate, supported by the rapid development of new hardware and software optimized for ML.

Although AI is not quite ready yet for clinical application, its potential is staggering. The current health-care environment is characterized by a tremendous proliferation of unstructured data. These fused streams consisting of vast amounts of multimodal, multiscale data could be integrated and interpreted holistically through AI to develop a deep understanding of how molecular, cellular and tissue structure and biological data could relate to normal and diseased tissue function. In the next few years, this integration will enable a shift from human thinking to mathematical algorithms that are trained to perform complex tasks capable of interpreting enormous amounts of information in milliseconds.

These ML algorithms have the potential to provide efficient and high-speed throughput of diagnostics, to improve the accuracy of prognostics and to potentially even lower overall health-care costs. Such an integrated approach will require careful selection of patient data and associated biomarkers of clinical utility AI developments will by default initially take place at big centres where substantial computing resources and large, carefully labelled data sets are available.

Machine (Deep) Learning Methods for Image Processing and Radiomics - IEEE Journals & Magazine

However, once they are developed, the use of trained algorithms will not require significant computing resources and could be performed on a mobile device or by access to limited local computing or cloud resources. Specialized AI-based software for image-guided, real-time, intraoperative modifications will require collaboration with equipment suppliers for example, Intuitive, the maker of da Vinci robots and appropriate regulatory approvals before expanding to most operating rooms.

Considerable hurdles beyond computing power and data streams must be overcome before we can implement ML widely and integrate it within the pathological or radiological clinical workflows. For AI algorithms to be trained and perform adequately, the data and their labels need to be as consistent as possible. Implementation of ML is still hindered by differences in imaging equipment and protocols across centres, as well as by differences in diagnostic guidelines and errors in annotations of the data. These issues can be mitigated by preprocessing of the data for automatic corrections and normalization and by augmenting the data with a wide range of examples, including AI-generated synthetic examples, in order to make it robust to such differences.

During the learning process, labels need to be determined by experts who can accurately predict ground truth and must be automatically assigned with confidence levels based on their impact on the training of the algorithm. Images could potentially contain nuances that only a human with extensive training can identify. Humans have intuitions, experience, common sense and ability to understand context and question results, but the computer is not burdened by human limits of vision or cognition, fallibility related to stress, fatigue, hunger, learned bias or institutional pressures. A computer will always make its decisions purely on the basis of what it has learned from the data.

Here, caution is required, as algorithms that learn from human decisions will also learn from human mistakes. The steps needed to integrate ML into the clinic are still unknown, as is the nature of the tools that should be provided to the radiologist, pathologist, surgeon, urologist or other specialist in order to support working seamlessly with an underlying AI system. How the new algorithms will influence the diagnosis and management of our patients remains our decision.

In radiology departments and in pathology laboratories with ever-increasing workloads, AI could improve the quality, efficiency and throughput of large numbers of images and specimens. The computer could reliably separate slides with benign disease from those with cancer, leaving the suspicious slides for human review.

Thus, human experts will always be required to teach AI systems how to perform, to ensure that these systems are operating as designed and to deal with unintended consequences such as overdiagnosis, underdiagnosis or misdiagnosis in an expeditious manner. AI will augment — but not replace — human expertise as pathologists and radiologists will need to continuously monitor the outputs for errors in interpretation and incorrect leaps of logic.

Urologists need to understand this burgeoning science and to acknowledge that ML requires collaboration with data scientists, computer researchers and engineers to optimize the data shown to the CNN and to develop highly accurate AI-based decision-support applications to improve patient care. Russell, S.

McGinnis, D. What is the fourth industrial revolution? Hodges, A. Science , — Friedman, T. Darcy, A. Machine learning and the profession of medicine. JAMA , — Duda, R. Bishop, C. Nelder, J. Breiman, L. Random forests. Khurd, P. Computer-aided Gleason grading of prostate cancer histopathological images using texton forests. IEEE Int.

Imaging , — Doyle, S. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinformatics 13 , Gorelick, L. Prostate histopathology: learning tissue component histograms for cancer detection and classification. Imaging 32 , — Jolliffe, I. Goodfellow, I. Seligson, D. Global histone modification patterns predict risk of prostate cancer recurrence. Nature , This study uses unsupervised learning techniques to identify markers of recurrence of prostate cancer.

Thananjeyan, B. Russ, J. LeCun, Y. Deep learning. This paper provides an overview of DL and its many applications by three pioneers in the field. Press, Backpropagation applied to handwritten zip code recognition. Neural Comput. Litjens, G. A survey on deep learning in medical image analysis. Image Anal. This article reviews the major DL concepts pertinent to medical image analysis and summarizes over contributions to the field.

Shen, D. Deep learning in medical image analysis. Suzuki, K. Overview of deep learning in medical imaging. Alam, I. Emerging intraoperative imaging modalities to improve surgical precision. Imaging Biol. Angermueller, C.

Machine Learning and Medical Imaging

Deep learning for computational biology. Systems Biol. Madabhushi, A. Image analysis and machine learning in digital pathology: challenges and opportunities. This paper reviews both handcrafted feature extraction and DL approaches for histopathological image analysis and discusses digital pathology as a bridge between radiology and genomics. Nir, G. Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images. JAMA Netw. Open 2 , e Karimi, D.

Cootes, T. Active shape models-their training and application.


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Image Underst. Milletari, F. Dice, L. Measures of the amount of ecologic association between species. Ecology 26 , — A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Zeng, Q. Prostate segmentation in transrectal ultrasound using magnetic resonance imaging priors. Anas, E. A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

Hu, Y. Weakly-supervised convolutional neural networks for multimodal image registration. Computer-aided detection of prostate cancer in MRI. IEEE Trans. Imaging 33 , — Moradi, M. Multiparametric MRI maps for detection and grading of dominant prostate tumors. Imaging 35 , — Liu, S. Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. SPIE Int. Shiradkar, R. Boussion, N. Predicting the number of seeds in LDR prostate brachytherapy using machine learning and patients [abstract PO].

Kalan, S. History of robotic surgery. Kassahun, Y. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. This article discusses current and future ML applications in surgical robotics. Yip, M. Robot autonomy for surgery. Yang, G. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy.

Robot 2 , Ji, J. Mohareri, O. Intraoperative registered transrectal ultrasound guidance for robot-assisted laparoscopic radical prostatectomy. Samei, G. Real-time FEM-based registration of 3D to 2. Imaging 37 , — Teber, D. Augmented reality: a new tool to improve surgical accuracy during laparoscopic partial nephrectomy? If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website. Thanks in advance for your time. Skip to content.

Search for books, journals or webpages All Pages Books Journals. View on ScienceDirect. Hardcover ISBN: Imprint: Academic Press. Published Date: 9th August Page Count: For regional delivery times, please check When will I receive my book? Sorry, this product is currently out of stock. Flexible - Read on multiple operating systems and devices.

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By contrast, deep learning, a machine learning technique that learns its own features, has been tremendously successful in identifying obj Nature Biomedical Engineering Objective: Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses, which often starts with autorefraction to estimate the refractive error. In this study, using deep learning, we trained a network to estimate refractive error from fundus photos only.

Design: Retrospective analysis. Methods, Intervention, or Testing: Refractive error Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls.

The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the weal Afshar , Sam S. Gross , Lizzie Dorfman , Cory Y.

McLean , Mark A. Nature Biotechnology We consider three hypotheses concerning the primate neocortex which have influenced computational neuroscience in recent years. Is the mind modular in terms of its being profitably described as a collection of relatively independent functional units? Does the regular structure of the cortex imply a single algorithm at work, operating on many different inputs in parallel?

Can the cognitive differences between humans and our closest primate relatives be explained in terms of a scalable cortical architecture? We bring to bear diverse sources of evidence to argue that the answers to each of these questions - with some judicious qualifications - a Predictive modeling with electronic health record EHR data is anticipated to drive personalized medicine and improve healthcare quality.

We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple cen This paper presents a convolutional neural network CNN approach for segmenting gigapixel pathology images into normal and cancerous pixels to aid breast cancer diagnosis. Each year, the treatment decisions for more than , patients in the U. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues.

This is labor intensive and error-prone. Our method leverag Imaging is a central method in life sciences, and the drive to extract information from microscopy approaches has led to methods to fluorescently label specific cellular constituents. However, the specificity of fluorescent labels varies, labeling can confound biological measurements, and spectral overlap limits the number of labels to a few that can be resolved simultaneously.

ISL predicts different labels in multiple cell types from indep The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits e. This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions.

Human Molecular Genetics Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.

Image Classification

Evolutionary algorithms provide a technique to discover such networks automatically. Despite significant computational requirements, we show that evolving models that rival large, hand-designed architectures is possible today. To do this, we use novel and intuitive mutation operators that navigate large search spa Vision loss from diabetic retinopathy should be unnecessary for patients with access to diabetic retinopathy screening, yet it still occurs at high rates and in varied contexts. Our mixed-methods research addressed the contexts of care and treatment seeking in a sample of people with VTDR using safety-net clinic services and eye specialist referrals.

We point to conceptual weaknesses in the Medical Anthropology IF 1. Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly.

Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a re Copy number variants CNVs are an important type of genetic variation and play a causal role in many diseases.