Open Access. Powered by Scholars. Published by Universities.®
Biomedical Engineering and Bioengineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 6 of 6
Full-Text Articles in Biomedical Engineering and Bioengineering
An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub
An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub
Computer Vision Faculty Publications
Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Tradi-tional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diag-nosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient med-ical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and …
Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub
Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub
Computer Vision Faculty Publications
Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through polymerase chain reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to many people. X-rays on the other hand have been readily used since the early 20th century and are relatively cheaper, quicker to obtain, and typically covered by health insurance. With a careful selection of model, hyperparameters, and augmentations, we show that it is possible to develop models with 83% accuracy in binary classification and 64% in multi-class …
Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub
Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub
Computer Vision Faculty Publications
Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types. Positron emission tomography and computed tomography are used to detect and segment the tumor region. Clinically, tumor segmentation is extensively time-consuming and prone to error. Machine learning, and deep learning in particular, can assist to automate this process, yielding results as accurate as the results of a clinician. In this research study, we develop a vision transformers-based method to automatically delineate H&N tumor, and compare its results to leading convolutional neural network (CNN)-based models. We use multi-modal data …
Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub
Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub
Computer Vision Faculty Publications
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep …
Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub
Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub
Computer Vision Faculty Publications
The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced …
Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub
Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub
Computer Vision Faculty Publications
Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmenta-tion as it is difficult to have clinical experts manually annotate large volumes of data. One such task is the segmentation of cardiac structures in ultrasound images of the heart. In this paper, we argue whether or not contrastive pretraining is helpful for the segmentation of the left ventricle in echocardiography images. Furthermore, we study the effect of this on two segmentation networks, DeepLabV3, as well as the commonly used segmentation net-work, UNet. Our …