Proceedings ArticleDOI
Recurrent Attention Mechanism Networks for Enhanced Classification of Biomedical Images
Mazhar Shaikh,Varghese Alex Kollerathu,Ganapathy Krishnamurthi +2 more
- pp 1260-1264
TLDR
Recurrent attention mechanism based network aid in reducing computational overhead while performing convolutional operations on highresolution images on high resolution images.Abstract:
Convolutional neural networks achieve state of the art results for a variety of tasks. However, this improved performance comes at the cost of performing convolutional operations throughout the entire image. Resizing of images to manageable levels is one of the often used techniques so as to reduce this computational overhead. On medical images, lesions are represented by a minuscule proportion of pixels and resizing may lead to loss of information. Recurrent attention mechanism based network aid in reducing computational overhead while performing convolutional operations on high resolution images. The proposed technique was tested on 2 distinct classification task viz; classification of brain tumors from Magnetic Resonance images & predicting the severity of diabetic macular edema from fundus images. For the former task $(n=300)$, the technique achieved state of the art accuracy of 97%. While on the latter $(n=89)$, the proposed model achieved an accuracy of 93.37%read more
Citations
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Journal ArticleDOI
Cross-attention multi-branch network for fundus diseases classification using SLO images
Hai Xie,Xianlu Zeng,Haijun Lei,Jie Du,Jiantao Wang,Guoming Zhang,Jiuwen Cao,Tianfu Wang,Baiying Lei +8 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra wide field view of 180-200˚.
Journal ArticleDOI
Recent trends and advances in fundus image analysis: A review
TL;DR: A comprehensive review of the state-of-the-art methods for the detection and segmentation of retinal image features is presented in this article , where several notable techniques for retinal features are categorized into essential groups and compared in depth.
Journal ArticleDOI
An automated brain tumor classification in MR images using an enhanced convolutional neural network
Journal ArticleDOI
Crowd Density Estimation by Using Attention Based Capsule Network and Multi-Column CNN
Merve Ayyuce Kizrak,Bulent Bolat +1 more
TL;DR: In this article, a two-column deep neural network architecture consisting of both CNN and capsule network-based attention modules was proposed to estimate the number of people in a crowd using static images or video images.
Journal ArticleDOI
Learning Multi-Level Features to Improve Crowd Counting
TL;DR: Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates the proposed FFANet outperforms the existing methods.
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Posted Content
Empirical evaluation of gated recurrent neural networks on sequence modeling
TL;DR: These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.