AIMC Topic: Image Processing, Computer-Assisted

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A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.

Scientific reports
Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time...

Current and emerging artificial intelligence applications for pediatric abdominal imaging.

Pediatric radiology
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep l...

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.

International journal of computer assisted radiology and surgery
PURPOSE: The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this i...

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

Toxicologic pathology
For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnost...

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.

Medical physics
PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T , T , and proton density (PD). The study aims to develop a multicontrast reconstruction method bas...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Scientific reports
Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation r...

Impact of image compression on deep learning-based mammogram classification.

Scientific reports
Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact o...

Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging.

EBioMedicine
BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of infl...

Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we aim to address issues of (1) joint spatial-temporal modeling and (2) side information injection for deep-learning based in-loop filter. For (1), we design a deep network with both progressive rethinking and collaborative learning me...

A survey on active learning and human-in-the-loop deep learning for medical image analysis.

Medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatmen...