17 citations · 17 across the 5 of their papers we have counts for
6 papers
Wave-GMS: Lightweight Multi-Scale Generative Model for Medical Image Segmentation
Talha Ahmed, Nehal Ahmed Shaikh, Hassan Mohy-ud-Din
For equitable deployment of AI tools in hospitals and healthcare facilities, we need Deep Segmentation Networks that offer high performance and can be trained on cost-effective GPU…
Towards robust radiomics and radiogenomics predictive models for brain tumor characterization
Maria Nadeem, Asma Shaheen, Muhammad F. A. Chaudhary +1
In the context of brain tumor characterization, we focused on two key questions: (a) stability of radiomics features to variability in multiregional segmentation masks obtained wit…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge
Syed Talha Bukhari, Hassan Mohy-ud-Din
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature in most top-performing CNNs is an encoder-…
Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images
Sana Jabbar, Syed Talha Bukhari, Hassan Mohy-ud-Din
We proposed a multi-view SA-LA model for simultaneous segmentation of RV on the short-axis (SA) and long-axis (LA) cardiac MR images. The multi-view SA-LA model is a multi-encoder,…
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
Syed Muhammad Anwar, Tooba Altaf, Khola Rafique +3
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistancein diagnosis of cancer, planning of treatment…