AIMC Topic: Image Processing, Computer-Assisted

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NiftyNet: a deep-learning platform for medical imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functional...

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Medical image analysis
Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate...

Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques.

Computers in biology and medicine
The objectives of this study are to assess various automated texture features obtained from the segmented colony regions of induced pluripotent stem cells (iPSCs) and confirm their potential for characterizing the colonies using different machine lea...

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

NeuroImage. Clinical
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...

Spatial-Temporal Recurrent Neural Network for Emotion Recognition.

IEEE transactions on cybernetics
In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial-temporal depend...

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

Physics in medicine and biology
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally ...

Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information.

Acta psychiatrica Scandinavica
OBJECTIVE: We investigated the potential of computer-based models to decode diagnosis and lifetime consumption in alcohol dependence (AD) from grey-matter pattern information. As machine-learning approaches to psychiatric neuroimaging have recently c...

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

Microscopy research and technique
A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treat...

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

Computational intelligence and neuroscience
One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new...

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal ...