scholarly journals Zero-shot neural decoding from rhesus macaque inferior temporal cortex using deep convolutional neural networks

2019 ◽  
Vol 19 (10) ◽  
pp. 209a
Author(s):  
Thomas P O’Connell ◽  
Marvin M Chun ◽  
Gabriel Kreiman
Keyword(s):  
Neural Networks ◽  
Rhesus Macaque ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Neural Decoding ◽  
Deep Convolutional Neural Networks

scholarly journals Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex

2019 ◽  
Author(s):  
Astrid A. Zeman ◽  
J. Brendan Ritchie ◽  
Stefania Bracci ◽  
Hans Op de Beeck
Keyword(s):  
Neural Networks ◽  
Visual Cortex ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Temporal Cortex ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Deep Convolutional Neural Networks ◽  
Shape Information ◽  
Category Information

AbstractDeep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with biological representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Image Captioning Using Deep Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 1712 ◽  
pp. 012015
Author(s):  
G. Geetha ◽  
T. Kirthigadevi ◽  
G.Godwin Ponsam ◽  
T. Karthik ◽  
M. Safa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Captioning ◽  
Deep Convolutional Neural Networks
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