106 citations · 294 across the 21 of their papers we have counts for
22 papers · 1 filter
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze +3
In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of…
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images
Mohammad Saeed Rad, Thomas Yu, Behzad Bozorgtabar +1
Existing reference (RF)-based super-resolution (SR) models try to improve perceptual quality in SR under the assumption of the availability of high-resolution RF images paired with…
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta +14
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entitie…
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation
Devavrat Tomar, Manana Lortkipanidze, Guillaume Vray +2
Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the traini…
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Valentin Anklin, Pushpak Pati, Guillaume Jaume +6
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have…
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
Antoine Spahr, Behzad Bozorgtabar, Jean-Philippe Thiran
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large…