Detection of Brain Metastases with Deep Learning Single-Shot Detector Algorithms
Menée à partir de données portant sur 266 patients traités entre 2011 et 2018 par radiochirurgie stéréotaxique (âge moyen : 60 ans), cette étude évalue la performance d'un modèle d'algorithme d'apprentissage automatique pour détecter des métastases cérébrales lors d'une IRM pondérée en T1 et planifier le traitement
Beginning in 2012, deep learning (DL), a variant of machine learning, has dominated competitions in image classification (ie, determining whether an image contains a cat, dog, or airplane). This class of algorithms was enabled by so-called convolutional neural networks (CNNs), a class of artificial neural networks (ANNs). ANNs are computing systems vaguely inspired by the biologic neural networks, and CNNs bear some similarity to biologic visual systems. In 2015, a DL method was said to achieve “superhuman” performance in large-scale image classification (1). CNN models have also performed well on semantic segmentation, or assigning labels to pixels or voxels in images. In 2016, a CNN system was shown to be statistically indistinguishable from human readers in the detection of lesions at mammography (2). Basic ANN technology was introduced in the 1940s (3), but it did not perform well on real problems. The current version of this technology has gained performance through the increase in the number of layers in the networks. Adding layers in the CNN has resulted in substantial growth of the needed computational power; this gain has been made practical by the relatively recent use of so-called graphic processing units for computations of this kind.
Radiology , commentaire, 2019