Convolutional neural networks for mammography mass lesion classification

Arévalo, John; González, Fabio A.; Ramos-Pollán, Raúl; Oliveira, José Luís; López, Miguel

doi:10.1109/embc.2015.7318482

Cited by 106 publications

(59 citation statements)

References 9 publications

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“…Examples include the identification and pathology of X-ray and computer tomography modalities [25], automatic classification of pulmonary perifissural nodules [41], pulmonary nodule detection [26], and mammography mass lesion classification [42]. Moreover, in [26], Van Ginneken et al show that the combination of CNNs features and classical features for pulmonary nodule detection can improve the performance of the model.…”

Section: Methodsmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

143

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Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

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Section: Methodsmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

143

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“…The recent research on transfer learning in medical imaging can be categorized into two groups. The first group [28]- [30] consists of works wherein a pre-trained CNN is used as a feature generator. Specifically, the pre-trained CNN is applied to an input image and then the CNN outputs (features) are extracted from a certain layer of the network.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Tajbakhsh

Shin

Gurudu

et al. 2016

IEEE Trans. Med. Imaging

2,516

1,325

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Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

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“…The authors in [28–31] presented the deep learning methods for solving most complex tasks in different areas of research in biology, bioinformatics, biomedicine, robotics, and 3D technologies.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

Sladojević

Arsenović

Anderla

et al. 2016

Computational Intelligence and Neuroscience

1,314

467

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The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

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Convolutional neural networks for mammography mass lesion classification

Cited by 106 publications

References 9 publications

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

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