stp2y

arXiv:2407.19274v1 Announce Type: new Abstract: Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with "more advanced" computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across diverse organs and modalities. The code is available at url{https://github.com/BailiangJ/rethink-reg}. Source link…

[Submitted on 30 Jul 2024] View a PDF of the paper titled Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language, by Hossein Rajaby Faghihi and 4 other authors View PDF Abstract:This paper presents a conversational pipeline for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. It leverages large language models to generate declarative programs in the DomiKnowS framework. The programs in this framework express concepts and their relationships as a graph in addition to logical constraints between them. The graph, later, can be connected to trainable neural models according to those specifications. Our proposed pipeline utilizes techniques like…