Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Samala, Ravi K.; Chan, Heang Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Kenny, H.; Richter, Caleb

doi:10.1088/1361-6560/aa93d4

Cited by 183 publications

(107 citation statements)

References 24 publications

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“…It appears that the DCNN was able to extract the relevant features despite the differences in the two types of images, whereas conventional image processing methods are generally more sensitive to these differences, as also observed from the larger drop in performance in DMs for the TFO method. This study further shows that DFMs can be effective supplemental training samples for DCNN in mammographic image analysis tasks when the primary image samples, for example, DMs, are limited as demonstrated in our previous study …”

Section: Discussionsupporting

confidence: 77%

“…This study further shows that DFMs can be effective supplemental training samples for DCNN in mammographic image analysis tasks when the primary image samples, for example, DMs, are limited as demonstrated in our previous study. 38 There are several limitations in this study. First, we used an unbalanced mix of DFM set and DM set for the training of DCNN.…”

Section: Discussionmentioning

confidence: 92%

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Automated pectoral muscle identification on MLO‐view mammograms: Comparison of deep neural network to conventional computer vision

Wei

Zhou

et al. 2019

Medical Physics

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Objectives The aim of this study was to develop a fully automated deep learning approach for identification of the pectoral muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed texture‐field orientation (TFO) method using conventional image feature analysis. Pectoral muscle segmentation is an important step for automated image analyses such as breast density or parenchymal pattern classification, lesion detection, and multiview correlation. Materials and methods Institutional Review Board (IRB) approval was obtained before data collection. A dataset of 729 MLO‐view mammograms including 637 digitized film mammograms (DFM) and 92 digital mammograms (DM) from our previous study was used for the training and validation of our deep convolutional neural network (DCNN) segmentation method. In addition, we collected an independent set of 203 DMs from 131 patients for testing. The film mammograms were digitized at a pixel size of 50 μm × 50 μm with a Lumiscan digitizer. All DMs were acquired with GE systems at a pixel size of 100 μm × 100 μm. An experienced MQSA radiologist manually drew the pectoral muscle boundary on each mammogram as the reference standard. We trained the DCNN to estimate a probability map of the pectoral muscle region on mammograms. The DCNN consisted of a contracting path to capture multiresolution image context and a symmetric expanding path for prediction of the pectoral muscle region. Three DCNN structures were compared for automated identification of pectoral muscles. Tenfold cross‐validation was used in training of the DCNNs. After training, we applied the ten trained models during cross‐validation to the independent DM test set. The predicted pectoral muscle region of each test DM was obtained as the mean probability map by averaging the ensemble of probability maps from the ten models. The DCNN‐segmented pectoral muscle was evaluated by three performance measures relative to the reference standard: (a) the percent overlap area (POA) of the pectoral muscle regions, (b) the Hausdorff distance (Hdist), and (c) the average Euclidean distance (AvgDist) between the boundaries. The results were compared to those obtained with the TFO method, used as our baseline. A two‐tailed paired t test was performed to examine the significance in the differences between the DCNN and the baseline. Results In the ten test partitions of the cross‐validation set, the DCNN achieved a mean POA of 96.5 ± 2.9%, a mean Hdist of 2.26 ± 1.31 mm, and a mean AvgDist of 0.78 ± 0.58 mm, while the corresponding measures by the baseline method were 94.2 ± 4.8%, 3.69 ± 2.48 mm, and 1.30 ± 1.22 mm, respectively. For the independent DM test set, the DCNN achieved a mean POA of 93.7% ± 6.9%, a mean Hdist of 3.80 ± 3.21 mm, and a mean AvgDist of 1.49 ± 1.62 mm comparing to 86.9% ± 16.0%, 7.18 ± 14.22 mm, and 3.98 ± 14.13 mm, respectively, by the baseline method. Conclusion In comparison to the TFO method, DCNN significantly improved the accuracy of pectoral muscle ...

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 92%

Automated pectoral muscle identification on MLO‐view mammograms: Comparison of deep neural network to conventional computer vision

Wei

Zhou

et al. 2019

Medical Physics

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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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“…Considering the obvious lack of large-scale annotated datasets, Medical Imaging community has already started exploiting 'transfer learning' [287], [288], [289]. In transfer learning, one can learn a complex model using data from a source domain where large-scale annotated images are available (e.g.…”

Section: Disentangling Medical Task Transfer Learningmentioning

confidence: 99%

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

et al. 2019

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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out 'lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.

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Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Cited by 183 publications

References 24 publications

Automated pectoral muscle identification on MLO‐view mammograms: Comparison of deep neural network to conventional computer vision

Automated pectoral muscle identification on MLO‐view mammograms: Comparison of deep neural network to conventional computer vision

An overview of deep learning in medical imaging focusing on MRI

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

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