AIMC Topic: Image Processing, Computer-Assisted

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Self-Training With Progressive Representation Enhancement for Unsupervised Cross-Domain Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two...

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

Magnetic resonance in medicine
PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination signi...

Utilizing Whole Slide Images for the Primary Evaluation and Peer Review of a GLP-Compliant Rodent Toxicology Study.

Toxicologic pathology
The approach undertaken to deliver a Good Laboratory Practice (GLP) validation of whole slide images (WSIs) and the associated workflow for the digital primary evaluation and peer review of a GLP-compliant rodent inhalation toxicity study is describe...

Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA.

American journal of human genetics
Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc ratio (VCDR) and vertical disc diameter (VDD). However, manual assessment often suffers from poor a...

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT.

IEEE transactions on medical imaging
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based...

Learning With Context Feedback Loop for Robust Medical Image Segmentation.

IEEE transactions on medical imaging
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less out...

Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation.

IEEE transactions on medical imaging
Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automati...

Pedestrian attribute recognition using two-branch trainable Gabor wavelets network.

PloS one
Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age...

Converting tabular data into images for deep learning with convolutional neural networks.

Scientific reports
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relation...

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

International journal of computer assisted radiology and surgery
PURPOSE: Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate dee...