scholarly journals Real-world applications of deep convolutional neural networks in diagnostic cancer imaging

2020 ◽  
Vol 9 (6) ◽  
pp. 82-82
Author(s):  
Hesham Elhalawani ◽  
Pei Yang ◽  
Mohamed Abazeed ◽  
Chirag Shah ◽  
Abdallah S. R. Mohamed ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Cancer Imaging ◽  
Deep Convolutional Neural Networks ◽  
Real World Applications

Retrieving real world clothing images via multi-weight deep convolutional neural networks

2017 ◽  
Vol 22 (S3) ◽  
pp. 7123-7134 ◽  
Author(s):  
Ruifan Li ◽  
Fangxiang Feng ◽  
Ibrar Ahmad ◽  
Xiaojie Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Deep Convolutional Neural Networks

scholarly journals EHANet: An Effective Hierarchical Aggregation Network for Face Parsing

2020 ◽  
Vol 10 (9) ◽  
pp. 3135 ◽  
Author(s):  
Ling Luo ◽  
Dingyu Xue ◽  
Xinglong Feng
Keyword(s):  
Neural Networks ◽  
Real World ◽  
State Of The Art ◽  
Contextual Information ◽  
Semantic Gap ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale ◽  
Hierarchical Aggregation ◽  
Real World Applications ◽  
Weighted Boundary

In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications.

scholarly journals Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects

2020 ◽  
Author(s):  
Timothy F. Brady ◽  
Viola S. Störmer
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Visual Working Memory ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Working Memory Capacity ◽  
Critical Role ◽  
Memory Capacity ◽  
Cognitive Systems ◽  
Deep Convolutional Neural Networks

The capacity of visual working and visual long-term memory play a critical role in theories of cognitive architecture and the relationship between memory and other cognitive systems. Here, we argue that before asking the question of how capacity varies across different stimuli or what the upper bound of capacity is for a given memory system, it is necessary to establish a methodology that allows a fair comparison between distinct stimulus sets and conditions. One of the most important factors determining performance in a memory task is target/foil dissimilarity. We argue that only by maximizing the dissimilarity of the target and foil in each stimulus set can we provide a fair basis for memory comparisons between stimuli. In the current work we introduce a new way to pick such foils objectively for complex, meaningful real-world objects by using deep convolutional neural networks, and we validate this using both memory tests and similarity metrics. Using this method, we then provide evidence that there is a greater capacity for real-world objects relative to simple colors in visual working memory; critically, we also show that this difference can be reduced or eliminated when non-comparable foils are used, potentially explaining why previous work has not always found such a difference. Our study thus demonstrates that working memory capacity is not fixed capacity but depends critically on the type of information that is remembered, and offers a solution of how to compare memory performance and other cognitive systems across different stimulus sets on common ground.

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.

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