Manchester Metropolitan University

About: Manchester Metropolitan University is a education organization based out in Manchester, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5435 authors who have published 16202 publications receiving 442561 citations. The organization is also known as: Manchester Polytechnic & MMU.

An internal model of a moving visual target in the lateral cerebellum

Abstract: In order to overcome the relatively long delay in processing visual feedback information when pursuing a moving visual target, it is necessary to predict the future trajectory of the target if it is to be tracked with accuracy. Predictive behaviour can be achieved through internal models, and the cerebellum has been implicated as a site for their operation. Purkinje cells in the lateral cerebellum (D zones) respond to visual inputs during visually guided tracking and it has been proposed that their neural activity reflects the operation of an internal model of target motion. Here we provide direct evidence for the existence of such a model in the cerebellum by demonstrating an internal model of a moving external target. Single unit recordings of Purkinje cells in lateral cerebellum (D2 zone) were made in cats trained to perform a predictable visually guided reaching task. For all Purkinje cells that showed tonic simple spike activity during target movement, this tonic activity was maintained during the transient disappearance of the target. Since simple spike activity could not be correlated to eye or limb movements, and the target was familiar and moved in a predictable fashion, we conclude that the Purkinje cell activity reflects the operation of an internal model based on memory of its previous motion. Such a model of the target's motion, reflected in the maintained modulation during the target's absence, could be used in a predictive capacity in the interception of a moving object.

Automatic mass detection in mammograms using deep convolutional neural networks

Abstract: With recent advances in the field of deep learning, the use of convolutional neural networks (CNNs) in medical imaging has become very encouraging. The aim of our paper is to propose a patch-based CNN method for automated mass detection in full-field digital mammograms (FFDM). In addition to evaluating CNNs pretrained with the ImageNet dataset, we investigate the use of transfer learning for a particular domain adaptation. First, the CNN is trained using a large public database of digitized mammograms (CBIS-DDSM dataset), and then the model is transferred and tested onto the smaller database of digital mammograms (INbreast dataset). We evaluate three widely used CNNs (VGG16, ResNet50, InceptionV3) and show that the InceptionV3 obtains the best performance for classifying the mass and nonmass breast region for CBIS-DDSM. We further show the benefit of domain adaptation between the CBIS-DDSM (digitized) and INbreast (digital) datasets using the InceptionV3 CNN. Mass detection evaluation follows a fivefold cross-validation strategy using free-response operating characteristic curves. Results show that the transfer learning from CBIS-DDSM obtains a substantially higher performance with the best true positive rate (TPR) of 0.98 ± 0.02 at 1.67 false positives per image (FPI), compared with transfer learning from ImageNet with TPR of 0.91 ± 0.07 at 2.1 FPI. In addition, the proposed framework improves upon mass detection results described in the literature on the INbreast database, in terms of both TPR and FPI.