Dermoscopic image retrieval based on rotation-invariance deep hashing.
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TLDR
In this article, a hybrid dilated convolution spatial attention module is proposed, which can focus on key information and suppress irrelevant information based on the complex morphological characteristics of dermoscopic images.About:
This article is published in Medical Image Analysis.The article was published on 2021-11-06 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Convolutional neural network & Computer science.read more
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Association of white matter volume with sleep quality: a voxel-based morphometry study.
TL;DR: In this article, the authors investigated the relationship between white matter volume and sleep quality and found that white matter is a crucial component in the structural neuroanatomy and brain white structure is associated with brain function, behavior and cognition.
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Verifiable speech retrieval algorithm based on KNN secure hashing
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Content-based medical image retrieval with opponent class adaptive margin loss
TL;DR: In this paper , a triplet-wise learning method is proposed to address the deficiencies of point-wise and pairwise learning in characterizing the similarity relationships between image classes, which can lead to suboptimal segregation of opponent classes and limited generalization performance.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Proceedings ArticleDOI
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju,Michael Cogswell,Abhishek Das,Ramakrishna Vedantam,Devi Parikh,Dhruv Batra +5 more
TL;DR: This work combines existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and applies it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures.
Posted Content
CBAM: Convolutional Block Attention Module
TL;DR: The proposed Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks, can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs.
Proceedings Article
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu,Vladlen Koltun +1 more
TL;DR: This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems.