Browsing by Author "Perre, Ana"
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- Lesion classification in mammograms using convolutional neural networks and transfer learningPublication . Perre, Ana; Alexandre, Luís A.; Freire, LuísConvolutional neural networks (CNNs) have recently been successfully used in the medical field to detect and classify pathologies in different imaging modalities, including in mammography. One disadvantage of CNNs is the need for large training datasets, which are particularly difficult to obtain in the medical domain. One way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labeled non-medical data, is subsequently fine-tuned using a smaller dataset of medical data. In this paper, we use such a transfer learning approach, which is applied to three different networks that were pre-trained using the Imagenet dataset. We investigate how the performance of these pre-trained CNNs to classify lesions in mammograms is affected by the use, or not, of normalized images during the fine-tuning stage. We also assess the performance of a support vector machine fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging.
- Lesion classification in mammograms using convolutional neural networks and transfer learningPublication . Perre, Ana; Alexandre, Luís A.; Freire, LuísComputer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early-stage cancers, decreasing the false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) is one example of deep learning algorithms that proved to be successful in image classification. In this paper, we aim to study the application of CNN's to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, which is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNN's and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.