Search Results for author: Purang Abolmaesumi

Found 25 papers, 14 papers with code

Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data

no code implementations27 Mar 2024 Mohamed Harmanani, Paul F. R. Wilson, Fahimeh Fooladgar, Amoon Jamzad, Mahdi Gilany, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification.

GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis

1 code implementation25 Aug 2023 Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang, Purang Abolmaesumi, Renjie Liao

Due to inter-observer variability in echo-based diagnosis, which arises from the variability in echo image acquisition and the interpretation of echo images based on clinical experience, vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification.

Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

1 code implementation13 Aug 2023 Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi

However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set.

Contrastive Learning

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.

Fairness

EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

1 code implementation23 Jul 2023 Masoud Mokhtari, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang, Renjie Liao

To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD).

Graph Representation Learning

TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision

1 code implementation3 Mar 2023 Mahdi Gilany, Paul Wilson, Andrea Perera-Ortega, Amoon Jamzad, Minh Nguyen Nhat To, Fahimeh Fooladgar, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature.

Self-Supervised Learning

Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound

no code implementations1 Nov 2022 Paul F. R. Wilson, Mahdi Gilany, Amoon Jamzad, Fahimeh Fooladgar, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.

Representation Learning Self-Supervised Learning

EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks

1 code implementation30 Aug 2022 Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao

In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos.

Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound

no code implementations21 Jul 2022 Mahdi Gilany, Paul Wilson, Amoon Jamzad, Fahimeh Fooladgar, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation.

Vocal Bursts Intensity Prediction

Class Impression for Data-free Incremental Learning

1 code implementation26 Jun 2022 Sana Ayromlou, Purang Abolmaesumi, Teresa Tsang, Xiaoxiao Li

Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.

Class Incremental Learning Incremental Learning

Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography

1 code implementation3 Feb 2021 Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg, Robert Rohling, Ken Gin, Purang Abolmaesumi, Teresa Tsang

In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.

One-Shot Learning Self-Supervised Learning

Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

no code implementations CVPR 2021 Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, Teresa Tsang

The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames.

PEP: Parameter Ensembling by Perturbation

no code implementations NeurIPS 2020 Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William M. Wells III

In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.

Variational Learning with Disentanglement-PyTorch

1 code implementation11 Dec 2019 Amir H. Abdi, Purang Abolmaesumi, Sidney Fels

Unsupervised learning of disentangled representations is an open problem in machine learning.

Disentanglement Scheduling

A Study into Echocardiography View Conversion

1 code implementation5 Dec 2019 Amir H. Abdi, Mohammad H. Jafari, Sidney Fels, Theresa Tsang, Purang Abolmaesumi

The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion.

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

no code implementations29 Nov 2019 Alireza Mehrtash, William M. Wells III, Clare M. Tempany, Purang Abolmaesumi, Tina Kapur

We make the following contributions: 1) We systematically compare cross entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples.

Image Segmentation Medical Image Segmentation +3

A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics

no code implementations26 Nov 2019 Amir H. Abdi, Purang Abolmaesumi, Sidney Fels

However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.

Disentanglement

GAN-enhanced Conditional Echocardiogram Generation

1 code implementation5 Nov 2019 Amir H. Abdi, Teresa Tsang, Purang Abolmaesumi

One of the most sought-after problems in echo is the segmentation of cardiac structures (e. g. chambers).

Segmentation

Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery

1 code implementation27 Jun 2019 Amir H. Abdi, Mehran Pesteie, Eitan Prisman, Purang Abolmaesumi, Sidney Fels

The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team.

Deep Neural Maps

1 code implementation16 Oct 2018 Mehran Pesteie, Purang Abolmaesumi, Robert Rohling

We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM).

Representation Learning

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

no code implementations25 Feb 2017 Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III

In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?

Domain Adaptation Lesion Segmentation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.