AIMC Topic: Image Processing, Computer-Assisted

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A Multi-branch Attention-based Deep Learning Method for ALS Identification with sMRI Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The structural Magnetic resonance imaging (sMRI) of spinal cord plays a significant role in the clinical diagnosis of Amyotrophic Lateral Sclerosis (ALS). But due to small cross-sectional area in the axial plane and long sagittal/coronal expansion of...

Intelligent Agent Planning for Optimizing Parallel MRI Reconstruction via A Large Language Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Parallel magnetic resonance imaging (pMRI) reconstruction needs tedious parameter tuning process for achieving optimal image quality. Although data-driven artificial intelligence (AI) has significantly improved pMRI reconstruction, knowledge-driven A...

DeepSeg: A transfer-learning segmentation tool for limited sample training of nonhuman primate MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Tissue segmentation of individual magnetic resonance imaging (MRI) is a fundamental step in building accurate head models for brain stimulation. In nonhuman primates (NHPs), due to limited sample size, site variability, and sub-optimal image quality,...

Multi-dataset Collaborative Learning for Liver Tumor Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation of biomedical images has emerged due to its potential in improving real-world clinical processes and has achieved great success in recent years thanks to the development of deep learning. However, it is the limited availability...

GANs-guided Conditional Diffusion Model for Synthesizing Contrast-enhanced Computed Tomography Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In contrast to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans can highlight discrepancies between abnormal and normal areas, commonly used in clinical diagnosis of focal liver lesions. However, the use of contrast age...

A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Spine segmentation in computed tomography (CT) images is critical for automatic analysis, especially when focusing on varied spinal anatomy. Despite having comprehensive annotations for normal vertebrae, many datasets do not encompass labeled fractur...

UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pseudo-labeling based semi-supervised learning (SSL) framework has proven highly successful in medical image analysis (MIA) by addressing the problem of a shortage of labeled samples. However, the existing SSL methods use a fixed or flexible confiden...

[Research progress of deep learning applications in mass spectrometry imaging data analysis].

Se pu = Chinese journal of chromatography
Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total...

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Anatomia, histologia, embryologia
Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient a...