AIMC Topic: Image Processing, Computer-Assisted

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AdaptFRCNet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation.

Medical image analysis
Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medica...

A deep learning framework for reconstructing Breast Amide Proton Transfer weighted imaging sequences from sparse frequency offsets to dense frequency offsets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Amide Proton Transfer (APT) technique is a novel functional MRI technique that enables quantification of protein metabolism, but its wide application is largely limited in clinical settings by its long acquisition time. One way to reduce the scanning...

Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Pathology, research and practice
Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a significant role in diagnostic pathology. However, the enormous data size makes it difficult to directly input these images into image processing units (GPU)...

Computationally Enabled Polychromatic Polarized Imaging Enables Mapping of Matrix Architectures that Promote Pancreatic Ductal Adenocarcinoma Dissemination.

The American journal of pathology
Pancreatic ductal adenocarcinoma (PDA) is a highly metastatic and lethal disease. In PDA, extracellular matrix (ECM) architectures, known as tumor-associated collagen signatures (TACSs), regulate invasion and metastatic spread in both early dissemina...

Estimating canopy leaf angle from leaf to ecosystem scale: a novel deep learning approach using unmanned aerial vehicle imagery.

The New phytologist
Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance and climate change responses. Traditional LAD measurement methods are tim...

Rethinking boundary detection in deep learning-based medical image segmentation.

Medical image analysis
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas rema...

Deep implicit optimization enables robust learnable features for deformable image registration.

Medical image analysis
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, existing DLIR methods forego many of the benefi...

Human visual perception-inspired medical image segmentation network with multi-feature compression.

Artificial intelligence in medicine
Medical image segmentation is crucial for computer-aided diagnosis and treatment planning, directly influencing clinical decision-making. To enhance segmentation accuracy, existing methods typically fuse local, global, and various other features. How...

A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models.

International journal of medical informatics
BACKGROUND: Medical imaging is a vital diagnostic tool that provides detailed insights into human anatomy but faces challenges affecting its accuracy and efficiency. Advanced generative AI models offer promising solutions. Unlike previous reviews wit...

Deformation-invariant neural network and its applications in distorted image restoration and analysis.

Neural networks : the official journal of the International Neural Network Society
Images degraded by geometric distortions pose a significant challenge to imaging and computer vision tasks such as object recognition. Deep learning-based imaging models usually fail to give accurate performance for geometrically distorted images. In...