Behzad Bozorgtabar

Computer Vision Group Leader & Lecturer at EPFL
Senior Scientist at CHUV-EPFL

email: behzad.bozorgtabar [at] epfl [dot] ch

RESEARCH UPDATES

CVPR 2023 paper: 'CrOC: Cross-View Online Clustering for Dense Visual Representation Learning'

CrOC

CVPR 2023 paper: 'TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation'

TeSLA

About Me

I'm a senior scientist at the Signal Processing Lab (LTS5) at the Swiss Federal Institute of Technology (EPFL), with a joint affiliation with the Lausanne University Hospital (CHUV) Department of Radiology, Lausanne, Switzerland. At the EPFL-LTS5, I am the computer vision leader for the medical imaging group. I am a member of European Lab for Learning & Intelligent Systems (ELLIS) and EPFL’s ELLIS Unit. I am also a research staff scientist at the Center for Biomedical Imaging (CIBM). Earlier, I was a Postdoctoral Researcher at IBM Research-Australia.

My principal research area lies at the intersection of computer vision and medical image analysis using machine learning techniques. I have a strong interest in domain adaptation/generalization and self-supervised methodologies on learning from limited data or labels, which I consider major avenues for innovation and impact for many vision-based applications. The ultimate goal of my research is to build next-generation intelligent machines that will tackle many of the most challenging problems in responsible AI, including reliability, generalization, and scarcity of data and annotation, enabling AI systems to be safely deployed in real-world high-stakes applications.

News

[July 2023] Our joint work with KU Leuven, AdaSim: Adaptive Similarity Bootstrapping for Self-Distillation, has received acceptance from ICCV 2023.
[July 2023] Our joint work with IBM Zurich Research Lab, Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer, has received acceptance from journal of Medical Image Analysis (MedIA).
[June 2023] Two papers have received acceptance from MICCAI 2023.
[Feb 2023] Our paper, TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation, has received acceptance from CVPR 2023.
[Feb 2023] Our paper, CrOC: Cross-View Online Clustering for Dense Visual Representation Learning, has received acceptance from CVPR 2023.

EPFL Computer Vision Talks

EPFL CV Talks. I am organizing the EPFL Computer Vision Talks
YouTube Channel

Citations

citations. A complete list of my publications and patents can be found at Google Scholar

Recent Selected Publications

Adasim *New* Adaptive Similarity Bootstrapping for Self-Distillation
Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars
ICCV 2023
paper · github

CrOC_main *New* CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar, Tinne Tuytelaars, Jean-Philippe Thiran
CVPR 2023
project page · paper · github

TeSLA_main *New* TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Devavrat Tomar, Guillaume Vray, Behzad Bozorgtabar, Jean-Philippe Thiran
CVPR 2023
project page · paper · github

Proto SF-OSDA *New* Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer
Guillaume Vray, Devavrat Tomar, Behzad Bozorgtabar, Jean-Philippe Thiran
arXiv 2023
paper

AMAE *New* AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
Behzad Bozorgtabar, Dwarikanath Mahapatra, Jean-Philippe Thiran
MICCAI 2023
paper

ScoreNet: Learning Non-Uniform Attention and Augmentation for
Transformer-Based Histopathological Image Classification
Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran
WACV 2023
project page · paper · github

ANOM Anomaly Detection and Localization Using Attention-Guided Synthetic Anomaly
and Test-Time Adaptation
Behzad Bozorgtabar, Dwarikanath Mahapatra, Jean-Philippe Thiran
BMVC 2022
paper

OptTTA OptTTA: Learnable Test-Time Augmentation for
Source-Free Medical Image Segmentation Under Domain Shift
Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran, Behzad Bozorgtabar
MIDL 2022 (Oral)
project page · paper · github

SRMA. Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning
Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
Christian Abbet, Linda Studer, Andreas Fischer, Heather Dawson, Inti Zlobec,
Behzad Bozorgtabar, Jean-Philippe Thiran
MedIA Journal 2022
paper · github

SST. Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray,
Mohammad Saeed Rad, Jean-Philippe Thiran
WACV 2022
paper · github

SegGini. Learning Whole-Slide Segmentation from Inexact and
Incomplete Labels using Tissue Graphs
Valentin Anklin, Pushpak Pati, Guillaume Jaume, Behzad Bozorgtabar,
Antonio Foncubierta-Rodríguez, Jean-Philippe Thiran, Mathilde Sibony,
Maria Gabrani, Orcun Goksel
MICCAI 2021
paper · github

SOoD. SOoD: Self-Supervised Out-of-Distribution Detection
Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types
Behzad Bozorgtabar, Guillaume Vray, Dwarikanath Mahapatra, Jean-Philippe Thiran
ICCVW 2021
paper · github

CVPR2021. Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez,
Florinda Feroce, Anna Maria Anniciello, Tilman Rau, Maria Gabrani,
Jean-Philippe Thiran, Orcun Goksel
CVPR 2021
paper · github

T-MI. Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation
Devavrat Tomar, Manana Lortkipanidze, Guillaume Vray,
Behzad Bozorgtabar, Jean-Philippe Thiran
IEEE T-MI 2021
paper · github

MIDL2021. Self-Rule to Adapt: Learning Generalized Features from Sparsely-Labeled Data
Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Phenotyping
Christian Abbet, Linda Studer, Andreas Fischer, Heather Dawson, Inti Zlobec,
Behzad Bozorgtabar, Jean-Philippe Thiran
MIDL 2021
paper · github

ISBI2021. Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
Antoine Spahr , Behzad Bozorgtabar, Jean-Philippe Thiran
ISBI 2021
paper· github

ISBI2021. Benefiting from Bicubically Down-Sampled Images for
Learning Real-World Image Super-Resolution
Mohammad Saeed Rad, Thomas Yu, Claudiu Musat, Hazım Kemal Ekenel,
Behzad Bozorgtabar, Jean-Philippe Thiran
WACV 2021
paper

SALAD. SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays
Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray, Jean-Philippe Thiran
MICCAI 2020
paper · github

Self-Rule. Divide-and-Rule: Self- Supervised Learning for Survival Analysis in Colorectal Cancer
Christian Abbet, Inti Zlobec, Behzad Bozorgtabar, Jean-Philippe Thiran
MICCAI 2020
paper · github

CVPR2020. Pathological Retinal Region Segmentation From OCT Images
Using Geometric Relation Based Augmentation
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, Ling Shao
CVPR 2020
paper

Syndemo. SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion
Behzad Bozorgtabar, Mohammad Saeed Rad, Dwarikanath Mahapatra, Jean-Philippe Thiran
ICCV 2019
paper · supplementary material

SROBB. SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
Mohammad Saeed Rad, Behzad Bozorgtabar, Urs-Viktor Marti,
Max Basler, Hazım Kemal Ekenel,
Jean-Philippe Thiran
ICCV 2019
paper · supplementary material

FG2019. Using Photorealistic Face Synthesis and Domain Adaptation
to Improve Facial Expression Analysis
Behzad Bozorgtabar, Mohammad Saeed Rad, Hazım Kemal Ekenel, Jean-Philippe Thiran
FG 2019
paper · github

CVIU2019. Learn to Synthesize and Synthesize to Learn
Behzad Bozorgtabar, Mohammad Saeed Rad, Hazım Kemal Ekenel, Jean-Philippe Thiran
CVIU 2019
paper · github

NeurIPS18. Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
Guillaume Jaume, Behzad Bozorgtabar, Hazım Kemal Ekenel,
Jean-Philippe Thiran, Maria Gabrani
NeurIPS 2018
paper

MSMCT. MSMCT: Multi-State Multi-Camera Tracker
Behzad Bozorgtabar, Roland Goecke
IEEE TCSVT 2018
paper

Grants

Personalized Health and Related Technologies (PHRT)

Swiss Cancer League

Discovery Translation Fund (DTF 2.0)

Teaching

2019-Present	Image analysis and pattern recognition (EE-451-4 ECTS- Bozorgtabar & Thiran), EPFL
2019-Present	Lab in signal and image processing (EE-490(f)-4 ECTS- Bozorgtabar & Thiran), EPFL