Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

Burt, Jeremy R.; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bağcı, Ulaş

doi:10.1259/bjr.20170545

Cited by 138 publications

(94 citation statements)

References 57 publications

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“…There are several thorough reviews and overviews of the field to consult for more information, across modalities and organs, and with different points of view and level of technical details. For example the comprehensive review [102] 27 , covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103,104,105,106,107,108,109,110,111,112], including radiomics and imaging genomics (radiogenomics) [113], and toolkits and libraries for deep learning [114]; deep learning in neuroimaging and neuroradiology [115]; brain segmentation [116]; stroke imaging [117,118]; neuropsychiatric disorders [119]; breast cancer [120,121]; chest imaging [122]; imaging in oncology [123,124,125]; medical ultrasound [126,127]; and more technical surveys of deep learning in medical image analysis [42,128,129,130]. Finally, for those who like to be hands-on, there are many instructive introductory deep learning tutorials available online.…”

Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,587

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,587

883

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“…The major difference between DL and traditional ANNs are the number of hidden layers [48]. ANNs are usually limited to three layers and they are known today as Shallow Neural Networks, whereas DL are NNs with many layers and they are referred to as Deep Neural Networks (DNNs) [52]. DNNs use multiple layers to explore more complex nonlinear, patterns and learn meaningful relationships within the data [51], and they learn and construct inherent features from each successive hidden layer of neurons, by minimizing or even removing the need for feature engineering.…”

Section: Deep Learning Fundamentals and Elementsmentioning

confidence: 99%

“…BD has not yet started to be widely used in some disciplines, and sometimes there are very limited databases in them. This lack of data to train DL architectures can produce a problem called overfitting, which occurs when a learning model memorizes the training data and it does not generalize well when faced with new data [52]. Even when large datasets are available, the complexity of DL models can make DNNs more prone to overfitting.…”

Section: Deep Learning Fundamentals and Elementsmentioning

confidence: 99%

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Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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“…Lately, deep learning modeling has shown great promise in many AI applications, including biomedical imaging analysis. Deep convolutional neural networks (CNNs) have been studied for analyzing mammographic images such as breast density category classification, [11][12][13][14][15] breast anatomy classification, 16 mass detection, [17][18][19][20] prediction, 21 and segmentation, 22 etc. 23,24 The unique nature of deep learning is that, massive data are fed to the CNN model which then automatically learns/extracts intrinsic imaging traits/features that are associated with the model output (i.e., outcome).…”

Section: Introductionmentioning

confidence: 99%

Deep learning modeling using normal mammograms for predicting breast cancer risk

et al. 2019

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Purpose: To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting. Methods: We conducted a retrospective Institutional Review Board-approved study on a case-control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case-control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer-free (controls) within the follow-up period. We implemented an end-to-end deep learning model and a GoogLeNet-LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric. Results: The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68-0.78; GoogLeNet-LDA model on CC view] when using the whole-breast and was 0.72 (95% CI: 0.67-0.76; GoogLeNet-LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet-LDA model significantly (all P < 0.05) outperformed the end-to-end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC = 0.54; 95% CI: 0.49-0.59). Conclusions: The proposed deep learning modeling approach can predict short-term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging-based breast cancer risk assessment.

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Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

Cited by 138 publications

References 57 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep learning modeling using normal mammograms for predicting breast cancer risk

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