AIMC Topic: Image Processing, Computer-Assisted

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A Video-Based DT-SVM School Violence Detecting Algorithm.

Sensors (Basel, Switzerland)
School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This ...

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR.

PloS one
Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware modu...

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation.

Physics in medicine and biology
Deep convolutional neural network (DCNN) has shown great success in various medical image segmentation tasks, including organ-at-risk (OAR) segmentation from computed tomography (CT) images. However, most studies use the dataset from the same source(...

CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation.

IEEE transactions on medical imaging
Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medi...

Artificial Intelligence in Dermatology: A Primer.

The Journal of investigative dermatology
Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical valid...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

Medical & biological engineering & computing
High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning ...

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module.

Sensors (Basel, Switzerland)
Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessar...

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission sc...

Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics.

Scientific data
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in c...

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.

BMJ (Clinical research ed.)
OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.