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544 results on '"Abolmaesumi, Purang"'

5. AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Author

Darbandsari, Amirali, Farahani, Hossein, Asadi, Maryam, Wiens, Matthew, Cochrane, Dawn, Khajegili Mirabadi, Ali, Jamieson, Amy, Farnell, David, Ahmadvand, Pouya, Douglas, Maxwell, Leung, Samuel, Abolmaesumi, Purang, Jones, Steven J. M., Talhouk, Aline, Kommoss, Stefan, Gilks, C. Blake, Huntsman, David G., Singh, Naveena, McAlpine, Jessica N., and Bashashati, Ali

Published
2024
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6. Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance

Author

Luong, Christina L., Jafari, Mohammad H., Behnami, Delaram, Shah, Yaksh R., Straatman, Lynn, Van Woudenberg, Nathan, Christoff, Leah, Gwadry, Nancy, Hawkins, Nathaniel M., Sayre, Eric C., Yeung, Darwin, Tsang, Michael, Gin, Ken, Jue, John, Nair, Parvathy, Abolmaesumi, Purang, and Tsang, Teresa

Published
2024
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26. Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

Author

Lu, Nelson, Vaseli, Hooman, Mahdavi, Mobina, Taheri Dezaki, Fatemah, Luong, Christina, Yeung, Darwin, Gin, Ken, Tsang, Michael, Nair, Parvathy, Jue, John, Barnes, Marion, Behnami, Delaram, Abolmaesumi, Purang, and Tsang, Teresa S. M.

Subjects
DEEP learning, ATRIAL fibrillation, ECHOCARDIOGRAPHY, HEART beat, ELECTROCARDIOGRAPHY, DIAGNOSIS, ARTIFICIAL intelligence
Abstract

Background: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. Methods: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training–validation–test split ratio. Results: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. Conclusions: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification. [ABSTRACT FROM AUTHOR]

Published
2024
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29. EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

Author

Mokhtari, Masoud, Mahdavi, Mobina, Vaseli, Hooman, Luong, Christina, Abolmaesumi, Purang, Tsang, Teresa S. M., and Liao, Renjie

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical labels, i.e., only a few landmark pixels in a high-dimensional image are annotated, leading many prior works to heavily rely on isotropic label smoothing. However, such a label smoothing strategy ignores the anatomical information of the image and induces some bias. To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss. We evaluate our model on a public and a private dataset under the in-distribution (ID) and out-of-distribution (OOD) settings. For the ID setting, we achieve the state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two datasets. Our model also shows better OOD generalization than prior works with a testing MAE of 4.3 mm., Comment: To be published in MICCAI 2023

Published
2023

37. Cardiac Phenotyping of SARS-CoV-2 in British Columbia: A Prospective Echo Study With Strain Imaging.

Author

Yim, Jeffrey, Tsang, Michael Y. C., Venkataraman, Anand, Balthazaar, Shane, Gin, Ken, Jue, John, Nair, Parvathy, Luong, Christina, Yeung, Darwin F., Moss, Robb, Virani, Sean A., McKay, Jane, Williams, Margot, Sayre, Eric C., Abolmaesumi, Purang, and Tsang, Teresa S. M.

Subjects
GLOBAL longitudinal strain, SPECKLE tracking echocardiography, SARS-CoV-2, VENTRICULAR ejection fraction, CORONARY artery disease, CARDIAC radionuclide imaging, FREE flaps
Abstract

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS. [ABSTRACT FROM AUTHOR]

Published
2023
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38. Semi-supervised learning from coarse histopathology labels.

Author

Fooladgar, Fahimeh, Nguyen Nhat to, Minh, Javadi, Golara, Sojoudi, Samira, Eshumani, Walid, Chang, Silvia, Black, Peter, Mousavi, Parvin, and Abolmaesumi, Purang

Subjects
SUPERVISED learning, HISTOPATHOLOGY, ULTRASONIC imaging, DATA scrubbing, MACHINE learning, EARLY detection of cancer
Abstract

Ultrasound imaging is commonly used to guide sampling the prostate tissue in transrectal biopsies, followed by detection of cancer through histopathological analysis and coarse labelling of sampled tissue. Ideally, the procedure should be improved by developing machine learning solutions that can identify the presence of cancer in ultrasound images to guide the biopsy procedure. Training a fully supervised learning model using coarse histopathology labels suffers from weakly annotated data which introduce label noise for each image pixel. To address this challenge, we propose a semi-supervised framework for learning with noisy labels. We leverage a two-component mixture model to cluster the training data into clean and noisy label samples based on their loss values. Then, during the semi-supervised training phase, we utilise the well-known MixMatch algorithm which incorporates consistency regularisation, entropy minimisation, and the Mixup regularisation as well as the cross-entropy loss function for noisy and clean sets, respectively. We evaluate the proposed framework with prostate ultrasound data obtained from 71 subjects, while sampling 264 biopsy cores. We achieve balanced accuracy, sensitivity, and specificity of 78.6%, 80.0%, and 77.1%, respectively. In a detailed comparison study, we demonstrate that our proposed framework outperforms the fully supervised method with state-of-the-art robust loss functions. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound

Author

Wilson, Paul F. R., Gilany, Mahdi, Jamzad, Amoon, Fooladgar, Fahimeh, To, Minh Nguyen Nhat, Wodlinger, Brian, Abolmaesumi, Purang, and Mousavi, Parvin

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of supervised learning methods. On the other hand, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centres, we demonstrate that feature representations learnt with this method can be used to classify cancer from non-cancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-supervised learning approach for prostate cancer detection using ultrasound data. 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.

Published
2022

47. EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks

Author

Mokhtari, Masoud, Tsang, Teresa, Abolmaesumi, Purang, and Liao, Renjie

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac ultrasound videos known as echocardiograms (echo) by manually tracing the left ventricle and estimating its volume on certain frames. These estimations exhibit high inter-observer variability due to the manual process and varying video quality. Such sources of inaccuracy and the need for rapid assessment necessitate reliable and explainable machine learning techniques. In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos. Our model first infers a latent echo-graph from the frames of one or multiple echo cine series. It then estimates weights over nodes and edges of this graph, indicating the importance of individual frames that aid EF estimation. A GNN regressor uses this weighted graph to predict EF. We show, qualitatively and quantitatively, that the learned graph weights provide explainability through identification of critical frames for EF estimation, which can be used to determine when human intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN achieves EF prediction performance that is on par with state of the art and provides explainability, which is crucial given the high inter-observer variability inherent in this task., To be published in MICCAI 2022

Published
2022

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

Author

Gilany, Mahdi, Wilson, Paul, Jamzad, Amoon, Fooladgar, Fahimeh, To, Minh Nguyen Nhat, Wodlinger, Brian, Abolmaesumi, Purang, and Mousavi, Parvin

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts, and noise all contribute to these difficulties. Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue imaging at high resolution. Our aim is to investigate the development of a robust deep learning model specifically for micro-ultrasound-guided prostate cancer biopsy. For the model to be clinically adopted, a key challenge is to design a solution that can confidently identify the cancer, while learning from coarse histopathology measurements of biopsy samples that introduce weak labels. METHODS: We use a dataset of micro-ultrasound images acquired from 194 patients, who underwent prostate biopsy. 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. We evaluate the performance of our model using the clinically relevant metric of accuracy vs. confidence. RESULTS: Our model achieves a well-calibrated estimation of predictive uncertainty with area under the curve of 88$\%$. The use of co-teaching and evidential deep learning in combination yields significantly better uncertainty estimation than either alone. We also provide a detailed comparison against state-of-the-art in uncertainty estimation.

Published
2022

50. Class Impression for Data-free Incremental Learning

Author

Ayromlou, Sana, Abolmaesumi, Purang, Tsang, Teresa, and Li, Xiaoxiao

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Standard deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in real-world clinical applications, where new classes are incrementally introduced through the addition of new data. Class incremental learning is a strategy allowing learning from such data. However, a major challenge is catastrophic forgetting, i.e., performance degradation on previous classes when adapting a trained model to new data. Prior methodologies to alleviate this challenge save a portion of training data require perpetual storage of such data that may introduce privacy issues. 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. Subsequently, it updates the model by combining the synthesized data with new class data. Furthermore, we incorporate a cosine normalized Cross-entropy loss to mitigate the adverse effects of the imbalance, a margin loss to increase separation among previous classes and new ones, and an intra-domain contrastive loss to generalize the model trained on the synthesized data to real data. We compare our proposed framework with state-of-the-art methods in class incremental learning, where we demonstrate improvement in accuracy for the classification of 11,062 echocardiography cine series of patients., To be appeared in MICCAI 2022

Published
2022

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