1 code implementation • 16 Jan 2024 • Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran, Behzad Bozorgtabar
This oversight leads to skewed BN statistics and undermines the reliability of the model under non-i. i. d.
2 code implementations • 10 Jul 2023 • Guillaume Vray, Devavrat Tomar, Jean-Philippe Thiran, Behzad Bozorgtabar
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories.
1 code implementation • CVPR 2023 • Devavrat Tomar, Guillaume Vray, Behzad Bozorgtabar, Jean-Philippe Thiran
Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain.
1 code implementation • 5 Oct 2021 • Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray, Mohammad Saeed Rad, Jean-Philippe Thiran
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data.
1 code implementation • 5 Mar 2021 • Devavrat Tomar, Manana Lortkipanidze, Guillaume Vray, Behzad Bozorgtabar, Jean-Philippe Thiran
We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
no code implementations • 19 Oct 2020 • Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray, Jean-Philippe Thiran
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario.