Deep Learning for Lung Lesion Detection
26 Aug 2018-pp 799-806
TL;DR: A pre-trained AlexNet (deep learning) framework is transferred to develop and implement a robust CAD system for the classification of lung images depending on whether they bear a lung lesion or not and high performances are reported.
Abstract: As the most fatal cancer type, early diagnosis of the lung cancer plays an important role for the survival of the patients. Diagnosis of the lung cancer involves screening the patients initially by Computed Tomography (CT) for the presence of lung lesions. This procedure requires expert radiologists which need to go over very large numbers of image slices manually in order to detect and diagnose lung lesions. Unfortunately this is a very time consuming process and its performance is very dependent on the performing radiologist. Thus assisting the radiologists by developing an automated computer aided detection (CAD) system is an interesting research goal. In this regard, as the aim of this paper a pre-trained AlexNet (deep learning) framework is transferred to develop and implement a robust CAD system for the classification of lung images depending on whether they bear a lung lesion or not. High performances of 98.72% sensitivity, 98.35% specificity and 98.48% accuracy are reported as a result.
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27 Aug 2020
TL;DR: In this paper, a review of deep learning-based automatic breast cancer detection systems and their performances are reviewed in detail for guiding the further research efforts in the field of computer-aided diagnosis.
Abstract: As one of the main unsolved problems of the current healthcare, cancer and one of its most frequent forms for women, breast cancer, is a popular research area in medicine. Early diagnosis is critical for improving the survival chances of breast cancer patients. Manual/visual diagnosis performed by a clinician is prone to many difficulties like high error rates and subjectivity. In addition, the time cost of the diagnosis procedure is very high and negatively effects the clinical routine. Automatic Computer-Aided Diagnosis (CAD) can be a solution to these problems. Recent research implementing deep learning-based techniques for developing an effective computer-aided breast cancer detection system proved to be very successful. In this paper, those recent deep learning-based automatic breast cancer detection systems and their performances are reviewed in detail for guiding the further research efforts in the field.
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Proceedings Article•
03 Dec 2012TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
73,978 citations
20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
49,639 citations
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
33,301 citations
TL;DR: This paper considered four distinct medical imaging applications in three specialties 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.
Abstract: 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.
2,294 citations
TL;DR: A review of MRI-based brain tumor segmentation methods using state-of-the-art algorithms with a focus on recent trend of deep learning methods.
489 citations