A5074585503- Medical Imaging and Analysis
- Bone and Joint Diseases
- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Advanced materials and composites
- Cryospheric studies and observations
- Hematological disorders and diagnostics
- Fire effects on ecosystems
- Dental Radiography and Imaging
- Landslides and related hazards
- Belt Conveyor Systems Engineering
- Spine and Intervertebral Disc Pathology
Tianjin University
2023-2024
Tianjin Hospital
2023-2024
Land Consolidation and Rehabilitation Center
2023
Modic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, interpreting these intricate images is a complex and time-consuming process. This study investigates performance of single shot multibox detector (SSD) ResNet18 network-based automatic detection MCs. Additionally, it compares inter-observer agreement observer-classifier in MCs diagnosis to validate feasibility deep learning network-assisted classified
Multi-turn dialogues are a key interaction method between humans and Large Language Models (LLMs), as conversations extend over multiple rounds, keeping LLMs' high generation quality low latency is challenge. Mainstream LLMs can be grouped into two categories based on masking strategy: causal LLM prefix LLM. Several works have demonstrated that tend to outperform ones in scenarios heavily depend historical context such multi-turn or in-context learning, thanks their bidirectional attention...
ABSTRACT Objective Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. However, it shows a high degree of sensitivity to variations MRI because its semiquantitative nature. In 2021, authors this further proposed quantitative and reliable MC grading method. automated tools grade MCs are lacking. This study developed investigated performance convolutional neural network (CNN)...