AIMC Topic: Neural Networks, Computer

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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning.

PloS one
We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx o...

Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation.

IEEE transactions on medical imaging
Automated whole breast ultrasound (ABUS) has been widely used as a screening modality for examination of breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective c...

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

International journal of neural systems
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging moda...

Complex-valued unsupervised convolutional neural networks for sleep stage classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be...

A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biologica...

Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.

Journal of dentistry
OBJECTIVES: Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to e...

Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks.

Neuron
Large-scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity. Understanding how low-dimen...

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training.

PloS one
This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in or...

Biomedical literature classification with a CNNs-based hybrid learning network.

PloS one
Deep learning techniques, e.g., Convolutional Neural Networks (CNNs), have been explosively applied to the research in the fields of information retrieval and natural language processing. However, few research efforts have addressed semantic indexing...

Dense Deconvolutional Network for Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics
Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the process of diagnosis and treatment of skin lesions. However, it is a challenging task due to the high variation of appearances and sizes of skin lesions. In o...