Publications by Seyedbehzad Bozorgtabar

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

IEEE2025

Robust semantic learning for precise medical image segmentation

Seyedbehzad Bozorgtabar

Precisely localizing anomalies in medical images remains a significant challenge due to their heterogeneous nature across modalities and organs. While initial efforts excelled in identifying prominent anomalies, detecting minute target lesions posed signif ...

2025

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

International Conference on Learning Representations, ICLR2025

Multi-Label Generalized Zero Shot Chest X-Ray Classification By Combining Image-Text Information With Feature Disentanglement (vol 44, pg 31, 2025)

Seyedbehzad Bozorgtabar

Presents corrections to the paper, (Corrections to "Multi-Label Generalized Zero Shot Chest X-Ray Classification By Combining Image-Text Information With Feature Disentanglement"). ...

Institute of Electrical and Electronics Engineers2025

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel scale of WSIs, conventional multiple ...

IEEE2025

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

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. We propose a practica ...

Institute of Electrical and Electronics Engineers2024

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Seyedbehzad Bozorgtabar

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Elsevier2024

Multi-Label Generalized Zero Shot Chest X-Ray Classification by Combining Image-Text Information With Feature Disentanglement

Seyedbehzad Bozorgtabar

In fully supervised learning-based medical image classification, the robustness of a trained model depends on its exposure to various disease classes. Generalized Zero Shot Learning (GZSL) aims to predict both seen and novel unseen classes. While most GZSL ...

Institute of Electrical and Electronics Engineers2024

Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification

Seyedbehzad Bozorgtabar

Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...

Springer International Publishing Ag2023

CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

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 consistency objective with an Online Clustering mechanism (CrOC) to discove ...

Los Alamitos2023

Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

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 views. Recent work NNCLR goes beyond the cross- view paradigm and uses positi ...

Ieee Computer Soc2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

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. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Guillaume Jaume, Pushpak Pati, Zeineb Ayadi, Kevin Thandiackal, Seyedbehzad Bozorgtabar, Maria Gabrani

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

ELSEVIER2023

AMAE: Adaptation of Pre-trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

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 annotation of anomaly data. However, nearly all existing methods are formulated ...

Springer International Publishing Ag2023

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Antoine Jérôme Spahr, Seyedbehzad Bozorgtabar

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

IEEE COMPUTER SOC2023

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Seyedbehzad Bozorgtabar, Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Jean-Philippe Thiran, Christian Robert Abbet, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecy ...

Pergamon-Elsevier Science Ltd2023

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Jean-Philippe Thiran, Mohammad Saeed Rad, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

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 the cohort's images is feasible, annotating all available images is impracti ...

IEEE COMPUTER SOC2022

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

PMLR2022

Annotation-efficient image anomaly detection

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

The present invention proposes a method for detecting anomalous or out-of-distribution images in a machine learning system (1) comprising a pre-training network with a first encoder, and an anomaly detection network with a second encoder. The system is fir ...

2022