no code implementations • 25 Mar 2024 • Ahmed Soliman, Yousif Yousif, Ahmed Ibrahim, Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok
In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well- known self-adaptive nnUNet framework with its configuration based on given data.
1 code implementation • Complex & Intelligent Systems 2024 • Ahmed Soliman, Samir Shaheen, Mayada Hadhoud
We evaluate the performance of these models on two datasets CoNaLa and DJANGO and compare them to existing state-of-the-art models.
Ranked #2 on Code Generation on CoNaLa