AIMC Topic: Pattern Recognition, Automated

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Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.

Medical image analysis
Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applica...

Image denoising using deep CNN with batch renormalization.

Neural networks : the official journal of the International Neural Network Society
Deep convolutional neural networks (CNNs) have attracted great attention in the field of image denoising. However, there are two drawbacks: (1) it is very difficult to train a deeper CNN for denoising tasks, and (2) most of deeper CNNs suffer from pe...

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel...

Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks.

Medical image analysis
Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could pot...

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...

Neural Probabilistic Graphical Model for Face Sketch Synthesis.

IEEE transactions on neural networks and learning systems
Neural network learning for face sketch synthesis from photos has attracted substantial attention due to its favorable synthesis performance. However, most existing deep-learning-based face sketch synthesis models stacked only by multiple convolution...

Exploring Duality in Visual Question-Driven Top-Down Saliency.

IEEE transactions on neural networks and learning systems
Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-dow...

Efficient IntVec: High recognition rate with reduced computational cost.

Neural networks : the official journal of the International Neural Network Society
In many deep neural networks for pattern recognition, the input pattern is classified in the deepest layer based on features extracted through intermediate layers. IntVec (interpolating-vector) is known to be a powerful method for this process of cla...

Automatic and heuristic complete design for ANFIS classifier.

Network (Bristol, England)
There is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership...

Identifying schizophrenia subgroups using clustering and supervised learning.

Schizophrenia research
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are...