Quantitative Analysis of Cervical Spinal Cord Microstructure and Macrostructure Using Deep Learning-based Segmentation of Magnetic Resonance Images
This research aims to investigate the relationships between microstructural and macrostructural features of the cervical spinal cord in a healthy population using quantitative MRI analysis, and to develop a deep learning-based segmentation framework for accurate measurement of macrostructural characteristics.
Pathological Clue-driven Representation Learning for Accurate Brain CT Report Generation
Leveraging diverse pathological clues, including segmented regions, entities, and report themes, to build fine-grained cross-modal representations and seamlessly transfer them to enhance the quality of generated brain CT reports.
Cascading Refinement CNN for Semantic Segmentation of Myocardial Pathologies from Multi-Sequence Cardiac MRI
A cascading refinement CNN model, MS-CaRe-CNN, can semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema from multi-sequence cardiac MRI data, enabling accurate assessment of myocardial viability.
Improving Liver Vessel Segmentation in Medical Images through Joint Encoding of Shape and Topology Priors
Incorporating both shape and topological priors into a unified latent representation improves the accuracy and anatomical consistency of automated liver vessel segmentation in medical images.
Enhancing Liver Vessel Segmentation through Scale-Specific Auxiliary Multi-Task Contrastive Learning
A novel deep learning approach that leverages scale-specific auxiliary tasks and contrastive learning to effectively capture the complex multi-scale geometry of the liver vascular tree.
Automated Quantification of Knee Cartilage Shape and Lesion Using Deep Learning
A deep learning-powered toolbox, CartiMorph Toolbox (CMT), for automated quantification of knee cartilage shape and lesion from medical images.
Enhancing Lumbar Spine MRI Segmentation with Advanced Deep Learning and Targeted Data Preprocessing
This study presents an advanced approach to accurately segment lumbar spine structures, including vertebrae, spinal canal, and intervertebral discs, in MRI scans using deep learning techniques. The key innovations include a robust data preprocessing pipeline, a modified U-Net model with architectural enhancements, and a custom combined loss function to effectively handle class imbalance.
Clinical Prior Guided Hierarchical Vision-Language Pre-training for Medical Imaging Analysis
A novel clinical prior guided hierarchical vision-language pre-training framework, IMITATE, that aligns multi-level visual features from medical images with the descriptive and conclusive textual features from hierarchical medical reports, outperforming state-of-the-art methods across various medical imaging downstream tasks.
Leveraging Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation
A novel self-supervised approach, MedSASS, that leverages the intrinsic properties of medical images to enhance binary semantic segmentation performance, outperforming existing state-of-the-art self-supervised techniques.
A Comprehensive Benchmark for Adapting Vision Transformers to Diverse Medical Imaging Tasks
The core message of this work is to introduce a large-scale medical visual task adaptation benchmark (Med-VTAB) and a novel Gated Mixture-of-Experts Adapter (GMoE-Adapter) to enhance the adaptability of pre-trained Vision Transformers (ViTs) to a broad spectrum of medical imaging tasks.
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