AIMC Topic: Image Processing, Computer-Assisted

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An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Human brain mapping
Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regress...

Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure.

eLife
Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report o...

Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning.

IEEE transactions on neural networks and learning systems
Neural decoding, which aims to predict external visual stimuli information from evoked brain activities, plays an important role in understanding human visual system. Many existing methods are based on linear models, and most of them only focus on ei...

Unsupervised pathology detection in medical images using conditional variational autoencoders.

International journal of computer assisted radiology and surgery
PURPOSE: Pathology detection in medical image data is an important but a rather complicated task. In particular, the big variability of the pathologies is a challenge to automatic detection methods and even to machine learning methods. Supervised alg...

Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI.

Computer methods and programs in biomedicine
OBJECTIVE: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).

P_VggNet: A convolutional neural network (CNN) with pixel-based attention map.

PloS one
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU de...

Visualization Methods for Image Transformation Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) are powerful machine learning models that have become the state of the art in several problems in the areas of computer vision and image processing. Nevertheless, the knowledge of why and how these models present ...

Deep embeddings for novelty detection in myopathy.

Computers in biology and medicine
We address the challenge of finding anomalies in ultrasound images via deep learning, specifically applying this to screening for myopathies and finding rare presentations of myopathic disease. Among myopathic diseases, this study focuses on the use ...

Learning-based CBCT correction using alternating random forest based on auto-context model.

Medical physics
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and p...