AIMC Topic: Image Processing, Computer-Assisted

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Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.

IEEE reviews in biomedical engineering
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advan...

Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning.

International dental journal
INTRODUCTION AND AIM: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accur...

Deep one-class probability learning for end-to-end image classification.

Neural networks : the official journal of the International Neural Network Society
One-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negative samples with a model trained via only positive samples or one-class annotated samples. With the d...

SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost o...

Semi-supervised Strong-Teacher Consistency Learning for few-shot cardiac MRI image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular disease is a leading cause of mortality worldwide. Automated analysis of heart structures in MRI is crucial for effective diagnostics. While supervised learning has advanced the field of medical image segmenta...

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.

Nature methods
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...

LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation.

Methods (San Diego, Calif.)
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy ...

Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.

Biomedical engineering online
BACKGROUND: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinic...

Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis.

BMC bioinformatics
BACKGROUND: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information...