I am currently an undergraduate student at Shandong First Medical University (2024-2028), majoring in Medical Information Engineering. I have a strong passion for learning and research in the interdisciplinary field of Medicine + Engineering. My current focus is on Deep Learning for Medical Image Segmentation. I have a basic understanding of Computer Vision (e.g., traditional digital image processing) and are familiar with terms and concepts in Computer Graphics (e.g., Volume Rendering, Surface Rendering, 3D Gaussian Splatting, NeRF, etc.).

• Medical Image IO & Analysis: Capable of using tools like pydicom/nibabel to read and manipulate medical data formats such as .dcm/.nii. Process medical images using tools like OpenCV and SimpleITK.
• Deep Learning for Medical Segmentation: Complete basic full-process model construction using PyTorch. (Currently, I do not write advanced custom differentiable layers from scratch). I use MONAI primarily as an API toolkit to call advanced models (e.g., SwinUNet) and have not yet studied it systematically.
• Reconstruction for Medical Imaging: This area involves high mathematical barriers, particularly Linear Algebra and Calculus/Differentiation, which I am still studying. I only understand basic algorithm principles (e.g., Surface/Volume Rendering) and have done some practical work.
• Computer Science Basics: Capable of using Ubuntu OS, connecting to servers via SSH to run deep learning models, but cannot yet configure multi-GPU training.
• Literature Research: Capable of using academic tools such as Google Scholar, PubMed, Zotero, Connected Papers, etc., to search for and read articles.
• Programming Languages: Mastered: Python; C++ (Future Plan).

Primarily for learning and exchange. I hope to meet more like-minded people. If you are interested in academic research collaboration, please contact me via email or WeChat. My capabilities are limited, but I am willing to give my best effort.

I haven't published any research papers yet, but maybe soon? I don't have any high-value repositories yet, but I hope to have great ones in the future.

Always open to discussing academic cooperation, open-source projects, or tech exchanges!

• Portfolio: https://zlyd-cv.github.io/
• Email: zhoulyd@126.com | zhouluoyidi@gmail.com
• WeChat: zlyd-CV

目前就读于山东第一医科大学（2024-2028），本科专业为医学信息工程。对医学+工科领域有着强烈的学习和研究热情。目前主要学习深度学习模型在医学图像分割；初步学习了计算机视觉基础（如传统数字图像处理方法）；了解一些计算机图形学相关名词与概念（如体绘制,面绘制,3D高斯溅射,神经辐射场等）。

• 医学图像读取与分析: 能够使用如 pydicom/nibabel 进行 .dcm/.nii 医学数据格式的读取与相关操作，通过 OpenCV, SimpleITK 等图像处理工具处理医学影像。
• 深度学习模型用于医学图像分割: 使用 PyTorch 完成简单模型全流程构建。（但是不会深入使用如手写可微分层）。MONAI 被我用来当作调用先进模型（如 SwinUNet）API 的工具包，还没有系统性学习。
• 计算机视觉/计算机图形学用于医学影像重建: 相关部分涉及较高的数学门槛，尤其是线性代数与高数微分知识，还在学习中。只了解过简单算法（如面绘制/体绘制）原理与做过相关实践。
• 计算机基础: 能够使用 Ubuntu OS 的计算机，并使用 SSH 连接服务器跑深度学习模型，但是不会跨显卡跑模型。
• 文献基础: 能够使用谷歌学术, PubMed, Zotero, Connected Papers 等相关学术工具搜索并阅读文章。
• 掌握的编程语言: Python；C++ (未来计划)。

主要还是学习交流为主，希望能够认识更多有志同道合的人。如果有相关的学术研究合作意向可通过邮件或者微信联系我，我虽能力有限但愿意拼尽全力。

还没有发表过相关的研究论文，但是也许快了？还没有具有较大价值的仓库，希望未来能够拥有好的仓库。

随时欢迎探讨学术合作、开源项目或技术交流！

• 个人主页: https://zlyd-cv.github.io/(暂时停止更新)
• Email: zhoulyd@126.com | zhouluoyidi@gmail.com
• WeChat: zlyd-CV