AIMC Topic: Image Processing, Computer-Assisted

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Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.

Hellenic journal of nuclear medicine
OBJECTIVE: The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emission tomography (F-FDG PET) at lateralizing seizure foci and diagnosing patients with unilateral temp...

Deep Endoscopic Visual Measurements.

IEEE journal of biomedical and health informatics
Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is ess...

Predicting Athlete Ground Reaction Forces and Moments From Spatio-Temporal Driven CNN Models.

IEEE transactions on bio-medical engineering
The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded...

Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks.

Computational intelligence and neuroscience
In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the...

An open-source tool for analysis and automatic identification of dendritic spines using machine learning.

PloS one
Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuron...

Synthesizing retinal and neuronal images with generative adversarial nets.

Medical image analysis
This paper aims at synthesizing multiple realistic-looking retinal (or neuronal) images from an unseen tubular structured annotation that contains the binary vessel (or neuronal) morphology. The generated phantoms are expected to preserve the same tu...

Weakly-supervised convolutional neural networks for multimodal image registration.

Medical image analysis
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspo...

Environmental properties of cells improve machine learning-based phenotype recognition accuracy.

Scientific reports
To answer major questions of cell biology, it is often essential to understand the complex phenotypic composition of cellular systems precisely. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impo...

Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks.

IEEE journal of biomedical and health informatics
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for var...