AIMC Topic: Deep Learning

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Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.

Journal of medical systems
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense mic...

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification.

Scientific reports
The increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classi...

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

IEEE journal of biomedical and health informatics
Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities t...

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

Methods (San Diego, Calif.)
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingl...

The capacity of feedforward neural networks.

Neural networks : the official journal of the International Neural Network Society
A long standing open problem in the theory of neural networks is the development of quantitative methods to estimate and compare the capabilities of different architectures. Here we define the capacity of an architecture by the binary logarithm of th...

EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine.

Journal of neuroscience methods
BACKGROUND: The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and ...

A comparative study of deep learning architectures on melanoma detection.

Tissue & cell
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instrumen...

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinicall...

Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning.

Journal of cardiovascular computed tomography
In the last decade, technical advances in the field of medical imaging significantly improved and broadened the application of coronary CT angiography (CCTA) for the non-invasive assessment of coronary artery disease. Recently, similar breakthroughs ...