A method for a computer system to perform deep transfer learning for radiotherapy treatment planning, wherein the method comprises: obtaining a base deep learning engine that is pre-trained, based on base training data, to perform a base radiotherapy treatment planning task, wherein the base deep learning engine includes multiple base layers; based on the base deep learning engine and target training data, generating a target deep learning engine to perform a target radiotherapy treatment planning task by performing at least one of the following: configuring a variable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine by modifying the variable base layer, by modifying base weight data associated with respective base layers to obtain corresponding target weight data; and configuring an invariable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer, by training target layers based on said feature data; characterized in that generating the target deep learning engine comprises: based on the base deep learning engine that is pre-trained to perform the base radiotherapy treatment planning task associated with a base anatomical site, generating the target deep learning engine to perform the target radiotherapy treatment planning task associated with a target anatomical site different from the base anatomical site. obtaining a base deep learning engine that is pre-trained, based on base training data, to perform a base radiotherapy treatment planning task, wherein the base deep learning engine includes multiple base layers; based on the base deep learning engine and target training data, generating a target deep learning engine to perform a target radiotherapy treatment planning task by performing at least one of the following: configuring a variable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine by modifying the variable base layer, by modifying base weight data associated with respective base layers to obtain corresponding target weight data; and configuring an invariable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer, by training target layers based on said feature data; characterized in that generating the target deep learning engine comprises: configuring a variable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine by modifying the variable base layer, by modifying base weight data associated with respective base layers to obtain corresponding target weight data; and configuring an invariable base layer among the multiple base layers of the base deep learning engine, and generating one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer, by training target layers based on said feature data; characterized in that generating the target deep learning engine comprises: based on the base deep learning engine that is pre-trained to perform the base radiotherapy treatment planning task associated with a base anatomical site, generating the target deep learning engine to perform the target radiotherapy treatment planning task associated with a target anatomical site different from the base anatomical site. <EOS>