Recherche

Afficher tous les résultats pour

Accueil

Personne

Seyedbehzad Bozorgtabar

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (38)

Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection

Jean-Philippe Thiran, Christian Robert Abbet, Seyedbehzad Bozorgtabar

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this is ...

ELSEVIER2022

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

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

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

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Seyedbehzad Bozorgtabar, Thomas Yu

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image de ...

2021

Quantifying Explainers of Graph Neural Networks in Computational Pathology

Jean-Philippe Thiran, Guillaume Jaume, Seyedbehzad Bozorgtabar

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities ...

IEEE COMPUTER SOC2021

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

Jean-Philippe Thiran, Antoine Jérôme Spahr, Seyedbehzad Bozorgtabar

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

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

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

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

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 training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021

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

Self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentation

Seyedbehzad Bozorgtabar

Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annot ...

Springer International Publishing2021

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

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

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

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

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

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

2020

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

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

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

2020

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

Jean-Philippe Thiran, Christian Robert Abbet, Seyedbehzad Bozorgtabar, Inti Zlobec

2020

Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

Medical image segmentation is an important task forcomputer aided diagnosis. Pixelwise manual annotationsof large datasets require high expertise and is time consum-ing. Conventional data augmentations have limited benefitby not fully representing the unde ...

2020

Learn to synthesize and synthesize to learn

Jean-Philippe Thiran, Mohammad Saeed Rad, Hazim Kemal Ekenel, Seyedbehzad Bozorgtabar

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data ...

2019

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Jean-Philippe Thiran, Mohammad Saeed Rad, Seyedbehzad Bozorgtabar

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

IEEE COMPUTER SOC2019

Page 2 sur 2