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SDCT-AuxNet: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis.

Medical image analysis
Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are...

Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks.

Medical image analysis
Thyroid cancer is a disease in which the first symptom is a nodule in the thyroid region of the neck. It is one of the cancers with the highest incidences, and has the highest increase rate in the last thirty years. Ultrasonography is one of the most...

Domain-invariant interpretable fundus image quality assessment.

Medical image analysis
Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing ...

Customization scenarios for de-identification of clinical notes.

BMC medical informatics and decision making
BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been ...

Automatic detection of rare pathologies in fundus photographs using few-shot learning.

Medical image analysis
In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, w...

Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data.

PloS one
Determining intrinsic number of clusters in a multidimensional dataset is a commonly encountered problem in exploratory data analysis. Unsupervised clustering algorithms often rely on specification of cluster number as an input parameter. However, th...

Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size.

Scientific reports
Deep neural networks have gained immense popularity in the Big Data problem; however, the availability of training samples can be relatively limited in specific application domains, particularly medical imaging, and consequently leading to overfittin...

And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model.

PloS one
Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimed...

Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT.

Scientific reports
Segmentation is fundamental to medical image analysis. Recent advances in fully convolutional networks has enabled automatic segmentation; however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a...

Improved protein structure prediction using potentials from deep learning.

Nature
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...