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Found 5 papers, 3 papers with code
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset
Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
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Deep learning in radiology: an overview of the concepts and a survey of the state of the art
In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms.
A systematic study of the class imbalance problem in convolutional neural networks
In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities.
