In this subsection, we first describe the methods for the induction of ODTs. The set of optimized ODTs (generated by the GA) forms an OmniGA model. Finally, we describe the different add-ons for an OmniGA model induction, which as an ensemble constitute the OmniGA framework. ...
The errors we encountered with Cellpose can be attributed to both neural network output and mask reconstruction. In Omnipose, we specifically addressed these two issues via the distance field and suppressed Euler integration, respectively, yielding a remarkably precise and generalizable image segmentation...
Xu, H., Li, C., Zhang, L., Ding, Z., Lu, T., Hu, H.: Immunotherapy efficacy prediction through a feature re-calibrated 2.5 d neural network. Comput. Methods Prog. Biomed. 249, 108135 (2024) Google Scholar Xu, H., Zhang, Y., Sun, L., Li, C., Huang, Y., Ding, X.:...
This repository contains code for the paper MSI-NeRF: Linking Omni-Depth with View Synthesis through Multi-Sphere Image aided Generalizable Neural Radiance Field.. Install conda env create -f environment.yml conda activate msinerf Data and Checkpoint ...
While the primary objective of this work is to propose a generalizable and scalable framework, incorporating specialized text encoding methods may further improve accuracy in specific medical applications. Future work will also focus on enhancing the scalability and computational efficiency of our framework...