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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-for-medical-diagnosis
课程评论：没有评论
课程名称：AI在医学诊断中的应用 课程概述：人工智能正在改变医疗实践，帮助医生更准确地诊断病人，预测病人的未来健康状况，并推荐更好的治疗方案。本课程适合那些已经了解一些与AI算法相关的数学和编程知识，并渴望进一步提升技能以应对医疗行业挑战的学习者，无需具备医学背景！本课程将为您提供在现代医学中应用前沿机器学习技术的实用经验。 课程内容包括： - **第一部分**：计算机视觉下的疾病检测，通过神经网络对胸部X光进行疾病分类。 - **第二部分**：模型评估，实施标准评估指标以检查模型在疾病诊断中的表现。 - **第三部分**：MRI图像的图像分割，准备3D MRI数据，实施适当的损失函数，以及应用预训练的U-net模型对3D脑MRI图像中的肿瘤区域进行分割。 该课程超越深度学习的基础知识，让学员深入了解将AI应用于医疗案例的细微差别。为了成功参与本课程，学习者应对AI算法背后的数学和编程有一定了解。尽管不需要成为AI专家，但中级Python编程能力和对深度神经网络（尤其是卷积网络）的基本知识是必需的。如果您对机器学习或神经网络相对陌生，我们建议您先参加deeplearning.ai提供的深度学习专业课程，由Andrew Ng主讲。 对于具备技能和知识的AI从业者，医疗领域的需求正在迅速增长。加入我们，共同开始构建未来医疗的旅程。
Part: 1
Title:Disease Detection with Computer Vision
Description:By the end of this week, you will practice classifying diseases on chest x-rays using a neural network.
Part: 2
Title:Evaluating Models
Description:By the end of this week, you will practice implementing standard evaluation metrics to see how well a model performs in diagnosing diseases.
Part: 3
Title:Image Segmentation on MRI Images
Description:By the end of this week, you will prepare 3D MRI data, implement an appropriate loss function for image segmentation, and apply a pre-trained U-net model to segment tumor regions in 3D brain MRI images.
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required! This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.
