Published in WACV2023, 2023
We propose a novel approach based on reverse knowledge distillation to train large models with limited data while preventing overfitting for retinal image registration. Read more
Published in IEEE Transactions on Biomedical and Health Informatics, 2023
This paper investigates the impact of segmentation onmedical image registration. Read more
Published in The 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023, 2023
This paper introduces robust sample specific loss functions for histology image analysis. Read more
Published in 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023), 2023
This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images Read more
Published in IEEE International Symposium on Biomedical Imaging (ISBI) 2022, 2022
In this paper we propose an attention-based deep learning framework that automatically analyzes renal tumors by fusing both the clinical and imaging features for a more accurate classification of kidney abnormalities. Read more
Published in 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
In this paper, we present a super-resolution SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in producing sharp-looking details in SR. Read more
Published in ESMO MAP, 2021
This paper proposes a pre-processing autoencoder that is trained adversarially to the sources of domain variations for mitotic figures detection in histological tumor images. Read more
Published in Arxiv, 2019
This paper introduces a machine learning-based method for fully automated diagnosis of sickle cell disease of poor-quality unstained images of a mobile microscope. Read more