{"url":"/method/models-genesis","slug":"models-genesis","name":"Models Genesis","full_name":"Models Genesis","full_name_withheld":false,"description_markdown":"**Models Genesis**, or **Generic Autodidactic Models**, is a self-supervised approach for learning 3D image representations. The objective of Models Genesis is to learn a common image representation that is transferable and generalizable across diseases, organs, and modalities.  It consists of an encoder-decoder architecture with skip connections in between, and is trained to learn a common image representation by restoring the original sub-volume $x\\_{i}$ (as ground truth) from the transformed one $\\bar{x}\\_{i}$ (as input), in which the reconstruction loss (MSE) is computed between the model prediction $x'\\_{0}$ and ground truth $x\\_{i}$. Once trained, the encoder alone can be fine-tuned for target classification tasks; while the encoder and decoder together can be fine-tuned for target segmentation tasks.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Models Genesis","paper":"/paper/models-genesis","first_author":"Zongwei Zhou","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/models-genesis"},"source":{"url":"https://arxiv.org/abs/2004.07882v4","title":"Models Genesis","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"3D Representations","url":"/methods/category/3d-representations","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/models-genesis","title":"Models Genesis","date":"2020-04-09","arxiv_id":"2004.07882","n_code_links":2,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/anatomy","name":"Anatomy","papers":1},{"task":"/task/medical-image-analysis","name":"Medical Image Analysis","papers":1},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/models-genesis"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}