This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

The main idea behind this paper is to highlight the background and current movements of International Accounting Standards Board (IASB) and Financial Accounting Standards Board (FASB) toward convergence of International Financial Reporting Standards (IFRS) and US Generally Accepted Accounting Principles (GAAP). Subsequent to introductory chapter the second chapter discusses the historical perspectives describing the various events that shaped the IASB and FASB current efforts towards convergence of IFRS and US GAAP. The third chapter describes the strategies policies and procedures adopted by the IASB/FASB to achieve the goal for international accounting convergence. The focus is on the maintainability of investors' interest and integrity of capital markets that can only be achieved by observing methods required for high quality accounting standards. Fourth chapter identifies the substantive differences and similarities between IFRS and US GAAP and catalogue those differences in which a specific accounting treatment is permissible under one basis of accounting standards but not permissible under the other.