Results 61 to 70 of about 9,899,096 (370)

Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

BMC Medical Informatics and Decision Making, 2019
Following publication of the original ...
Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier, Sheraz Ahmed   +6 more
doaj   +1 more source

Deep learning approach to scalable imaging through scattering media [PDF]

, 2019
We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published ...
Li, Yunzhe, Tian, Lei, Xue, Yujia
core   +1 more source

On "Deep Learning" Misconduct

Proceedings of the 3rd International Symposium on Automation, Information and Computing, 2022
Accepted by ISAIC 2022, 8 pages, three figures.
openaire   +2 more sources

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

BMC Medical Informatics and Decision Making, 2019
Background With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology.
Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier, Sheraz Ahmed   +6 more
doaj   +1 more source

Using Deep Learning for Image-Based Plant Disease Detection [PDF]

Frontiers in Plant Science, 2016
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure.
S. Mohanty, David P. Hughes, M. Salathé
semanticscholar   +1 more source

Accelerating Deep Learning with Shrinkage and Recall

, 2016
Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large.
Ding, Chris, Vishnu, Abhinav, Zheng, Shuai   +2 more
core   +1 more source

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

, 2020
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (
Carver, Eric, Chetty, Indrin, Elshaikh, Mohamed, Feldman, Aharon, Hearshen, David, Janic, Branislava, Lee, Joon, Liu, Chang, Movsas, Benjamin, Pantelic, Milan, Wen, Ning, Zong, Weiwei   +11 more
core   +1 more source

Deep learning? What deep learning?

South African Journal of Higher Education, 2003
In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability than those who adopt a surface ...
openaire   +3 more sources

Deep API learning

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016
Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences.
Xiaodong Gu, Dongmei Zhang, Sunghun Kim, Hongyu Zhang   +3 more
openaire   +3 more sources

Deep Neural Networks for Form-Finding of Tensegrity Structures

Mathematics, 2022
Analytical paradigms have limited conventional form-finding methods of tensegrities; therefore, an innovative approach is urgently needed. This paper proposes a new form-finding method based on state-of-the-art deep learning techniques.
Seunghye Lee, Qui X. Lieu, Thuc P. Vo, Jaehong Lee   +3 more
doaj   +1 more source