9,387 research outputs found
Deep Pyramidal Residual Networks
Deep convolutional neural networks (DCNNs) have shown remarkable performance
in image classification tasks in recent years. Generally, deep neural network
architectures are stacks consisting of a large number of convolutional layers,
and they perform downsampling along the spatial dimension via pooling to reduce
memory usage. Concurrently, the feature map dimension (i.e., the number of
channels) is sharply increased at downsampling locations, which is essential to
ensure effective performance because it increases the diversity of high-level
attributes. This also applies to residual networks and is very closely related
to their performance. In this research, instead of sharply increasing the
feature map dimension at units that perform downsampling, we gradually increase
the feature map dimension at all units to involve as many locations as
possible. This design, which is discussed in depth together with our new
insights, has proven to be an effective means of improving generalization
ability. Furthermore, we propose a novel residual unit capable of further
improving the classification accuracy with our new network architecture.
Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown
that our network architecture has superior generalization ability compared to
the original residual networks. Code is available at
https://github.com/jhkim89/PyramidNet}Comment: Accepted to CVPR 201
Advancements in Image Classification using Convolutional Neural Network
Convolutional Neural Network (CNN) is the state-of-the-art for image
classification task. Here we have briefly discussed different components of
CNN. In this paper, We have explained different CNN architectures for image
classification. Through this paper, we have shown advancements in CNN from
LeNet-5 to latest SENet model. We have discussed the model description and
training details of each model. We have also drawn a comparison among those
models.Comment: 9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International
Conference on Research in Computational Intelligence and Communication
Networks(ICRCICN 2018
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
Histopathological characterization of colorectal polyps is an important
principle for determining the risk of colorectal cancer and future rates of
surveillance for patients. This characterization is time-intensive, requires
years of specialized training, and suffers from significant inter-observer and
intra-observer variability. In this work, we built an automatic
image-understanding method that can accurately classify different types of
colorectal polyps in whole-slide histology images to help pathologists with
histopathological characterization and diagnosis of colorectal polyps. The
proposed image-understanding method is based on deep-learning techniques, which
rely on numerous levels of abstraction for data representation and have shown
state-of-the-art results for various image analysis tasks. Our
image-understanding method covers all five polyp types (hyperplastic polyp,
sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and
tubulovillous/villous adenoma) that are included in the US multi-society task
force guidelines for colorectal cancer risk assessment and surveillance, and
encompasses the most common occurrences of colorectal polyps. Our evaluation on
239 independent test samples shows our proposed method can identify the types
of colorectal polyps in whole-slide images with a high efficacy (accuracy:
93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method
in this paper can reduce the cognitive burden on pathologists and improve their
accuracy and efficiency in histopathological characterization of colorectal
polyps, and in subsequent risk assessment and follow-up recommendations
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