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Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.
PloS one
Jan 19, 2018
Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images ...
Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.
IEEE transactions on pattern analysis and machine intelligence
Jan 17, 2018
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sa...
A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.
Cell cycle (Georgetown, Tex.)
Jan 17, 2018
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficien...
Federated learning of predictive models from federated Electronic Health Records.
International journal of medical informatics
Jan 12, 2018
BACKGROUND: In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations an...
Deep Convolutional Neural Networks for breast cancer screening.
Computer methods and programs in biomedicine
Jan 11, 2018
BACKGROUND AND OBJECTIVE: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health c...
EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection.
IEEE transactions on pattern analysis and machine intelligence
Jan 10, 2018
In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers an...
ProtDet-CCH: Protein Remote Homology Detection by Combining Long Short-Term Memory and Ranking Methods.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 5, 2018
As one of the most challenging tasks in sequence analysis, protein remote homology detection has been extensively studied. Methods based on discriminative models and ranking approaches have achieved the state-of-the-art performance, and these two kin...
Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt.
Journal of neuroscience methods
Jan 2, 2018
Deep Visual Attention Prediction.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Dec 27, 2017
In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still ne...
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.
IEEE journal of biomedical and health informatics
Dec 25, 2017
Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is ...
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