Biologically inspired CNNs for Medical Imaging tasks

Medical image data poses several challenges for computer vision algorithms: it spans multiple imaging modalities and biological tissues, it contains several sources of noise and variation, and there is a scarcity of available labeled datasets. Some recent advances in computer vision models, such as the use of vision transformers and self-supervised learning have showed promising results in dealing with some of these challenges. However, it has not been tested whether the use of biologically inspired computations, another recent advanced in computer vision with considerable improvements in robustness, also translates to gains in medical imaging tasks. The goal of this project is to adapt the VOneNet family, a hybrid CNN with a front-end inspired and constrained by the primate primary visual cortex (V1), to multiple computer vision neural network architectures used for medical imaging tasks and to test their performance in a wide range of related benchmarks.