This project focuses on the intersection of deep learning and medical imaging, with saliency detection and multimodal medical image analysis as its core research directions. Addressing three core challenges in current medical image analysis — efficiency bottlenecks in anomaly detection, cross-modal semantic gaps, and insufficient adaptability for clinical deployment — the team leverages its prior research achievements in weakly supervised learning, multimodal fusion, and lightweight model design to carry out technical research on multimodal medical large models, cross-modal semantic alignment, and lightweight detection systems. It is dedicated to providing theoretical support and technical solutions for precise, real-time, and cost-effective clinical diagnostic assistance.
How it is being developed
Following the development philosophy of “research-driven and phased implementation”, the project is currently in the prototype development stage, with overall R&D advanced in three orderly phases:
1. Data & Baseline Construction Phase: Systematically integrate public multimodal medical imaging datasets (e.g., CT, MRI), complete data cleaning and standardization, reproduce and evaluate mainstream baseline models, and define performance benchmarks and optimization directions.
2. Core Algorithm Breakthrough Phase: Develop multimodal pre-training modules and cross-modal feature alignment mechanisms based on anatomical semantic constraints, complete algorithm implementation and comparative experiments, and verify technical effectiveness in few-shot anomaly detection scenarios.
3. Result Consolidation & Iteration Phase: Systematically review experimental data and technical innovations, advance academic paper writing and technical prototype optimization in parallel, and lay the foundation for subsequent clinical verification and achievement transformation.
Expected outcome
Short-term
1. Complete the standardization of multimodal medical imaging datasets and build a project-specific testing benchmark.
2. Develop a multimodal medical image anomaly detection algorithm prototype with few-shot adaptation capability, and verify the performance advantages of core technical innovations.
3. Complete the first draft of a high-quality academic paper, targeting submission to top international conferences in medical imaging or computer vision (e.g., MICCAI, ICCV).
Medium and Long-term Goals
1. Build an autonomous foundational medical large model supporting CT/MRI/ultrasound multimodal inputs, and form a multimodal medical image analysis framework with independent intellectual property rights.
2. Establish a large-scale multimodal medical imaging annotation dataset and a cross-modal benchmark testing platform.
3. Develop a lightweight intelligent medical image detection system, complete experimental clinical verification in primary healthcare scenarios, and promote the application of technology in inclusive medical scenarios.
Brief Introduction
This project focuses on the intersection of deep learning and medical imaging, with saliency detection and multimodal medical image analysis as its core research directions. Addressing three core challenges in current medical image analysis — efficiency bottlenecks in anomaly detection, cross-modal semantic gaps, and insufficient adaptability for clinical deployment — the team leverages its prior research achievements in weakly supervised learning, multimodal fusion, and lightweight model design to carry out technical research on multimodal medical large models, cross-modal semantic alignment, and lightweight detection systems. It is dedicated to providing theoretical support and technical solutions for precise, real-time, and cost-effective clinical diagnostic assistance.
How it is being developed
Following the development philosophy of “research-driven and phased implementation”, the project is currently in the prototype development stage, with overall R&D advanced in three orderly phases:
1. Data & Baseline Construction Phase: Systematically integrate public multimodal medical imaging datasets (e.g., CT, MRI), complete data cleaning and standardization, reproduce and evaluate mainstream baseline models, and define performance benchmarks and optimization directions.
2. Core Algorithm Breakthrough Phase: Develop multimodal pre-training modules and cross-modal feature alignment mechanisms based on anatomical semantic constraints, complete algorithm implementation and comparative experiments, and verify technical effectiveness in few-shot anomaly detection scenarios.
3. Result Consolidation & Iteration Phase: Systematically review experimental data and technical innovations, advance academic paper writing and technical prototype optimization in parallel, and lay the foundation for subsequent clinical verification and achievement transformation.
Expected outcome
Short-term
1. Complete the standardization of multimodal medical imaging datasets and build a project-specific testing benchmark.
2. Develop a multimodal medical image anomaly detection algorithm prototype with few-shot adaptation capability, and verify the performance advantages of core technical innovations.
3. Complete the first draft of a high-quality academic paper, targeting submission to top international conferences in medical imaging or computer vision (e.g., MICCAI, ICCV).
Medium and Long-term Goals
1. Build an autonomous foundational medical large model supporting CT/MRI/ultrasound multimodal inputs, and form a multimodal medical image analysis framework with independent intellectual property rights.
2. Establish a large-scale multimodal medical imaging annotation dataset and a cross-modal benchmark testing platform.
3. Develop a lightweight intelligent medical image detection system, complete experimental clinical verification in primary healthcare scenarios, and promote the application of technology in inclusive medical scenarios.