AIMC Topic: Image Processing, Computer-Assisted

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer.

Computers in biology and medicine
Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relat...

An appearance quality classification method for Auricularia auricula based on deep learning.

Scientific reports
The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster...

AI driven analysis of MRI to measure health and disease progression in FSHD.

Scientific reports
Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. Th...

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T.

Investigative radiology
OBJECTIVES: The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms.

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation.

Neural networks : the official journal of the International Neural Network Society
Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonabl...

Language-aware multiple datasets detection pretraining for DETRs.

Neural networks : the official journal of the International Neural Network Society
Pretraining on large-scale datasets can boost the performance of object detectors while the annotated datasets for object detection are hard to scale up due to the high labor cost. What we possess are numerous isolated filed-specific datasets, thus, ...

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification.

Medical image analysis
State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervise...

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

Scientific reports
The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cuttin...

Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-...

Cross-layer importance evaluation for neural network pruning.

Neural networks : the official journal of the International Neural Network Society
Filter pruning has achieved remarkable success in reducing memory consumption and speeding up inference for convolutional neural networks (CNNs). Some prior works, such as heuristic methods, attempted to search for suitable sparse structures during t...