Lin Lin

Title: Innovations in Lung Texture Recognition by Lin Lin

Introduction

Lin Lin is a prominent inventor based in Liaoning, China, known for his significant contributions to the field of image processing and computer vision. With a total of five patents to his name, Lin has focused on developing advanced methods for lung texture recognition, particularly in the context of computed tomography (CT) imaging.

Latest Patents

One of Lin Lin's latest patents is the "Deep network lung texture recognition method combined with multi-scale attention." This invention introduces a unique attention mechanism module and multi-scale feature fusion module designed to enhance the accuracy of recognizing typical textures of diffuse lung disease in CT images. The deep convolutional neural network constructed through this method achieves high-precision automatic recognition, making it clear, easy to construct, and implement.

Another notable patent is the "Unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition." This method allows a deep network model trained on one type of CT data to be effectively applied to another type without requiring manual labeling of typical lung textures. By utilizing an adversarial learning mechanism and a specially designed content consistency network module, this approach maintains high performance in lung texture recognition while saving development time and costs.

Career Highlights

Lin Lin is affiliated with Dalian University of Technology, where he continues to innovate and contribute to research in his field. His work has garnered attention for its practical applications and effectiveness in medical imaging.

Collaborations

Lin has collaborated with notable colleagues, including Xinchen Ye and Haojie Li, who have contributed to his research endeavors.

Conclusion

Lin Lin's innovative patents in lung texture recognition demonstrate his commitment to advancing medical imaging technology. His contributions are paving the way for more efficient and accurate diagnostic methods in healthcare.