AIMC Topic: Image Processing, Computer-Assisted
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A novel transfer learning framework for non-uniform conductivity estimation with limited data in personalized brain stimulation.
Physics in medicine and biology
May 6, 2025
. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that incorporate non-uniform conductivity to enable target-specific stimulation. Accurately estimating non-uniform conductivity in individualized head models r...
Improving Foundation Model for Endoscopy Video Analysis via Representation Learning on Long Sequences.
IEEE journal of biomedical and health informatics
May 6, 2025
Recent advancements in endoscopy video analysis have relied on the utilization of relatively short video clips extracted from longer videos or millions of individual frames. However, these approaches tend to neglect the domain-specific characteristic...
SWMA-UNet: Multi-Path Attention Network for Improved Medical Image Segmentation.
IEEE journal of biomedical and health informatics
May 6, 2025
In recent years, deep learning achieves significant advancements in medical image segmentation. Research finds that integrating Transformers and CNNs effectively addresses the limitations of CNNs in managing long-distance dependencies and understandi...
Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension.
IEEE journal of biomedical and health informatics
May 6, 2025
Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a promising way to improve the clinical diagnostic experience by significantly reducing scan times. While existing studies have used Generative Adversarial Networks to...
MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.
IEEE journal of biomedical and health informatics
May 6, 2025
Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there ...
PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.
IEEE journal of biomedical and health informatics
May 6, 2025
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...
MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis.
Sensors (Basel, Switzerland)
May 5, 2025
Significant advances have been made in the application of attention mechanisms to medical image segmentation, and these advances are notably driven by the development of the cross-axis attention mechanism. However, challenges remain in handling compl...
Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.
BMC medical imaging
May 5, 2025
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...
Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology.
Scientific reports
May 5, 2025
In digital pathology, the accurate detection, segmentation, and classification of cells are pivotal for precise pathological diagnosis. Traditionally, pathologists manually segment cells from pathological images to facilitate diagnosis based on these...
Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.
Scientific reports
May 5, 2025
Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack of diagnosis and appropriate management. The diagnosis of PCOD, along with proper classification with...
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