Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images

Cheng, Phillip M.; Malhi, Harshawn

doi:10.1007/s10278-016-9929-2

Cited by 207 publications

(118 citation statements)

References 15 publications

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“…Transfer learning has been extensively studied over the past few years, especially in the field of medical image analysis. 32,33 However, research that incorporates transfer learning model to deal with prostate cancer classification is sparse. 29,30 Moreover, these methods treated each prostate cancer image separately and ignored the image similarity information.…”

Section: B Transfer Learningmentioning

confidence: 99%

Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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Purpose Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. This study aims to learn discriminative features in prostate images and assist physicians in classifying prostate cancer automatically. Methods We develop a novel multiparametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pretrained model to compute features from multiparametric MRI images (mp‐MRI): T2w transaxial, T2w sagittal, and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp‐MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine‐tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability. Results Two cohorts: 132 cases from our institutional review board‐approved patient database and 112 cases from the PROSTATEx‐2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate cancer classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand‐crafted feature‐based methods and existing deep learning models in prostate cancer classification with higher accuracy. Conclusion The experiment results showed that the proposed method can learn discriminative features in prostate images and classify the cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine.

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Section: B Transfer Learningmentioning

confidence: 99%

Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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“…Unfortunately, generating several realizations of the geological model with standard geostatistical algorithms may be very challenging. One possible solution we intend to investigate in the future is to use data augmentation (Yaeger et al, 1997;Taylor and Nitschke, 2017) and transfer learning techniques (Hoo-Chang et al, 2018;Cheng and Malhi, 2017). Data augmentation consist of a series of affine transformations applied to the input data to increase the training set.…”

Section: Commentsmentioning

confidence: 99%

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Canchumuni

Emerick

Pacheco

2019

Computers & Geosciences

125

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History matching is a jargon used to refer to the data assimilation problem in oil and gas reservoirs. The literature about history matching is vast and despite the impressive number of methods proposed and the significant progresses reported in the last decade, conditioning reservoir models to dynamic data is still a challenging task. Ensemble-based methods are among the most successful and efficient techniques currently available for history matching. These methods are usually able to achieve reasonable data matches, especially if an iterative formulation is employed. However, they sometimes fail to preserve the geological realism of the model, which is particularly evident in reservoir with complex facies distributions. This occurs mainly because of the Gaussian assumptions inherent in these methods. This fact has encouraged an intense research activity to develop parameterizations for facies history matching. Despite the large number of publications, the development of robust parameterizations for facies remains an open problem.Deep learning techniques have been delivering impressive results in a number of different areas and the first applications in data assimilation in geoscience have started to appear in literature. The present paper reports the current results of our investigations on the use of deep neural networks towards the construction of a continuous parameterization of facies which can be used for data assimilation with ensemble methods. Specifically, we use a convolutional variational autoencoder and the ensemble smoother with multiple data assimilation. We tested the parameterization in three synthetic history-matching problems with channelized facies. We focus on this type of facies because they are among the most challenging to preserve after the assimilation of data. The parameterization showed promising results outperforming previous methods and generating well-defined channelized facies. However, more research is still required before deploying these methods for operational use.

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“…DL applied to brain magnetic resonance imaging has been used to distinguish patients with a first episode psychosis from controls, and predict lifetime alcohol consumption . DL has also been applied to ultrasound (USG); demonstrating high accuracy in detecting abdominal free fluid on FAST (focused assessment with sonography for trauma) scans, classifying abdominal USG images and providing automated analysis of ejection fraction on echocardiogram . DL has also enabled novel technologies, such as the use of a microwave based imaging helmet to accurately distinguish between ischaemic and haemorrhagic stroke in the prehospital environment …”

Section: Clinical Image Analysismentioning

confidence: 99%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

113

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Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

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Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images

Cited by 207 publications

References 15 publications

Prostate cancer classification with multiparametric MRI transfer learning model

Prostate cancer classification with multiparametric MRI transfer learning model

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Artificial intelligence and machine learning in emergency medicine

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