Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis

Levine, Adrian; Schlosser, Colin; Grewal, Jasleen K.; Coope, Robin; Jones, Steven J.M.; Yip, Stephen

doi:10.1016/j.trecan.2019.02.002

Cited by 159 publications

(100 citation statements)

References 73 publications

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“…As much as these high-throughput data acquisition approaches challenge the data-to-discovery process, they drive the development of new sophisticated computational methods for data analysis and interpretation. In particular, the synergy of cancer research and machine learning has led to groundbreaking discoveries in diagnosis, prognosis, and treatment planning for cancer patients (Vial et al, 2018;Levine et al, 2019). Typically, such machine learning methods are developed to address particular complexities inherent in individual data types, separately.…”

Section: Introductionmentioning

confidence: 99%

Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice

et al. 2019

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International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyze multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.

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Section: Introductionmentioning

confidence: 99%

Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice

et al. 2019

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“…Similar computational analyses from whole slide images obtained from liver biopsy specimens were also used to distinguish early well-differentiated HCC from non-cancerous hepatic tissues with a consistent classification (34). In summary, in the field of tumor pathology, advances in image processing and statistical methods have allowed greater refinement in the design of algorithms with an elevated potential in terms of diagnosis (35,36).…”

Section: Wsi In Hepatocellular Carcinoma Diagnosismentioning

confidence: 99%

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

et al. 2020

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Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra-and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

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“…22 Furthermore, these images could address a major issue in training deep neural networks for medical applications-the challenge in obtaining a sufficient amount of annotated training data for the model to capture high level features and prevent overfitting. 23 Data compilation and annotation for medical applications typically requires the involvement of highly trained experts, and enriching data sets with synthetic images can leverage the work of these experts to maximize training material and decrease overall time and cost requirements.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of diagnostic quality cancer pathology images

Peng

Farnell

et al. 2020

Preprint

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Keyboards: cancer, pathology, deep learning, artificial intelligence, quality assurance, education Link to the demo website: http://gan.aimlab.ca/ 1 ABSTRACT Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/ .

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Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis

Cited by 159 publications

References 73 publications

Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice

Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Synthesis of diagnostic quality cancer pathology images

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