AIMC Topic: Image Processing, Computer-Assisted

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Visualizing functional network connectivity differences using an explainable machine-learning method.

Physiological measurement
. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statisti...

Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow.

IEEE transactions on medical imaging
Accurate segmentation of cardiac structures in echocardiography videos is vital for diagnosing heart disease. However, challenges such as speckle noise, low spatial resolution, and incomplete video annotations hinder the accuracy and efficiency of se...

Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust g...

Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis.

IEEE transactions on medical imaging
Federated learning (FL) methods for multi-organ segmentation in CT scans are gaining popularity, but generally require numerous rounds of parameter exchange between a central server and clients. This repetitive sharing of parameters between server an...

Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction.

IEEE transactions on medical imaging
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...

UniAda: Domain Unifying and Adapting Network for Generalizable Medical Image Segmentation.

IEEE transactions on medical imaging
Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant discrepancies from seen (training) domains due to different vendors and scanning protocols. Existin...

Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images ba...

Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment.

IEEE transactions on neural networks and learning systems
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known...

Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.

BMC medical imaging
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...

A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network.

Scientific reports
Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. However, traditional multi-person pose estimation methods face challenges due to partial...