106 citations · 213 across the 42 of their papers we have counts for
8 papers · 1 filter
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping
Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar +2
Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when ap…
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
Behzad Bozorgtabar, Dwarikanath Mahapatra, Jean-Philippe Thiran
Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert anno…
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Guillaume Vray, Devavrat Tomar, Jean-Philippe Thiran +1
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shi…
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar +2
Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view…
TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Devavrat Tomar, Guillaume Vray, Behzad Bozorgtabar +1
Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information fro…
Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning
Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar +2
Most self-supervised methods for representation learning leverage a cross-view consistency objective i.e., they maximize the representation similarity of a given image's augmented…
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime
Thomas Stegmüller, Christian Abbet, Behzad Bozorgtabar +4
Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its wides…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…