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20222024
most citedAutomatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?

8 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV20241 cited

Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videos

Joseph Geo Benjamin, Mothilal Asokan, Amna Alhosani +5

Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, th…

cs.CV2024

SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast

Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani +3

Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.…

eess.IV20232 cited

MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models

Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi +2

The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medi…

eess.IV2023

Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation

Shahad Hardan, Hussain Alasmawi, Xiangjian Hou +1

Vestibular schwannoma (VS) is a non-cancerous tumor located next to the ear that can cause hearing loss. Most brain MRI images acquired from patients are contrast-enhanced T1 (ceT1…

eess.IV20228 cited

Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?

Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi +1

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…