I am using PyMIC for a classification task to determine whether the left ventricle (LV) has a scar or not. I noticed in the PyMIC examples that it mentions the use of VGG/ResNet architectures for the classification process. Does this imply that PyMIC's classification tasks are entirely based on VGG/ResNet models?
I initially assumed PyMIC would integrate UNet as part of the classification process, especially given its strong focus on medical image segmentation. Could you clarify if PyMIC uses UNet or any other specialized architectures in the classification workflow, or is it strictly limited to standard CNNs like VGG and ResNet for such tasks?
Thank you for the clarification!
I am using PyMIC for a classification task to determine whether the left ventricle (LV) has a scar or not. I noticed in the PyMIC examples that it mentions the use of VGG/ResNet architectures for the classification process. Does this imply that PyMIC's classification tasks are entirely based on VGG/ResNet models?
I initially assumed PyMIC would integrate UNet as part of the classification process, especially given its strong focus on medical image segmentation. Could you clarify if PyMIC uses UNet or any other specialized architectures in the classification workflow, or is it strictly limited to standard CNNs like VGG and ResNet for such tasks?
Thank you for the clarification!