AIMC Topic: Humans

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Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

Medical physics
PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) loc...

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

Medical physics
PURPOSE: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-bas...

Improving CBCT quality to CT level using deep learning with generative adversarial network.

Medical physics
PURPOSE: To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network.

Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm.

Sensors (Basel, Switzerland)
Ankle push-off occurs when muscle-tendon units about the ankle joint generate a burst of positive power at the end of stance phase in human walking. Ankle push-off mainly contributes to both leg swing and center of mass (CoM) acceleration. Humans use...

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Scientific reports
Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits t...

Hierarchical progressive learning of cell identities in single-cell data.

Nature communications
Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resol...

Deep learning in histopathology: the path to the clinic.

Nature medicine
Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been de...

EEG microstate features for schizophrenia classification.

PloS one
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes ...

Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence.

PloS one
PURPOSE: Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study w...

Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet.

PloS one
Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A re...