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Seyedbehzad Bozorgtabar

Cette personne a quitté l’EPFL

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Publications associées (38)

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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

Self-Taught Semi-Supervised Anomaly Detection On Upper Limb X-Rays

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Antoine Jérôme Spahr

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 number of annotations by radiologists, which is often prohibitively very ti ...

IEEE2021

ExprADA: Adversarial domain adaptation for facial expression analysis

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...

2020

Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Yu, Mohammad Saeed Rad

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 low-resolution (LR) inputs at testing. As the RF images should be similar i ...

IEEE COMPUTER SOC2021

Medical Image Classification Using Generalized Zero Shot Learning

Seyedbehzad Bozorgtabar

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learnin ...

IEEE COMPUTER SOC2021

SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types

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

The goal of out-of-distribution (OoD) detection is to identify unseen categories of inputs different from those seen during training, which is an important requirement for the safe deployment of deep neural networks in computational pathology. Additionally ...

IEEE COMPUTER SOC2021

Self-supervised Multimodal Generalized Zero Shot Learning for Gleason Grading

Seyedbehzad Bozorgtabar

Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict ...

Springer International Publishing2021

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

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller

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, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller

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

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller

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

Divide-and-Rule: Self- Supervised Learning for Survival Analysis in Colorectal Cancer

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Christian Robert Abbet

2020

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

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

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

Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slides. Ho ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

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

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

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

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

SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays

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

2020

Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

2020