Meta Learning with Medical Imaging and Health Informatics Applications

Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks' fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.

This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly.

元學習（Meta-Learning），或稱學習學習，近年來越來越受到歡迎。與為每個機器學習任務從頭開始建立人工智慧系統不同，元學習構建了計算機機制，以系統性和高效地適應新任務。元學習範式具有很大的潛力，可以解決深度神經網絡面臨的基本挑戰，如大量數據需求、計算昂貴的訓練以及在任務之間的轉移能力有限。

本書提供了元學習理論及其在醫學影像和健康信息學中的多樣應用的簡明摘要。它涵蓋了元學習的統一理論及其流行的變體，如模型無關學習、記憶增強、原型網絡和學習優化。本書匯集了機器學習和健康信息學領域的思想領袖，討論了元學習的當前狀態，以及它對醫學影像和健康信息學的相關性和未來發展方向。本書附帶一個GitHub存儲庫，其中包含各種代碼示例和文檔，以幫助讀者快速設置元學習算法以應用於他們的領域。