AIMC Topic: Image Processing, Computer-Assisted
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Predicting cell cycle stage from 3D single-cell nuclear-stained images.
Life science alliance
Apr 3, 2025
The cell cycle governs the proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound...
AI in SPECT Imaging: Opportunities and Challenges.
Seminars in nuclear medicine
Apr 3, 2025
SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial...
FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 3, 2025
PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although Convolutional Neural Networks (CNNs) have shown effectiveness in segmenting breast ultrasound images...
A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.
Computers in biology and medicine
Apr 3, 2025
Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by differ...
Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation.
IEEE transactions on medical imaging
Apr 3, 2025
Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contribution...
Exploring Contrastive Pre-Training for Domain Connections in Medical Image Segmentation.
IEEE transactions on medical imaging
Apr 3, 2025
Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA...
VBVT-Net: VOI-Based VVBP-Tensor Network for High-Attenuation Artifact Suppression in Digital Breast Tomosynthesis Imaging.
IEEE transactions on medical imaging
Apr 3, 2025
High-attenuation (HA) artifacts may lead to obscured subtle lesions and lesion over-estimation in digital breast tomosynthesis (DBT) imaging. High-attenuation artifact suppression (HAAS) is vital for widespread DBT applications in clinic. The convent...
Tackling Modality-Heterogeneous Client Drift Holistically for Heterogeneous Multimodal Federated Learning.
IEEE transactions on medical imaging
Apr 3, 2025
Multimodal Federated Learning (MFL) has emerged as a collaborative paradigm for training models across decentralized devices, harnessing various data modalities to facilitate effective learning while respecting data ownership. In this realm, notably,...
DEeR: Deviation Eliminating and Noise Regulating for Privacy-Preserving Federated Low-Rank Adaptation.
IEEE transactions on medical imaging
Apr 3, 2025
Integrating low-rank adaptation (LoRA) with federated learning (FL) has received widespread attention recently, aiming to adapt pretrained foundation models (FMs) to downstream medical tasks via privacy-preserving decentralized training. However, owi...
PASS: Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation.
IEEE transactions on medical imaging
Apr 3, 2025
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data. However, existing TTA solutions for segmentation tasks suffer fr...
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