2 citations · 6 across the 12 of their papers we have counts for
12 papers
FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation
Asim Ukaye, Numan Saeed, Karthik Nandakumar
Different CT segmentation datasets are typically obtained from different scanners under different capture settings and often provide segmentation labels for a limited and often dis…
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
Shahad Hardan, Darya Taratynova, Abdelmajid Essofi +2
Privacy preservation in AI is crucial, especially in healthcare, where models rely on sensitive patient data. In the emerging field of machine unlearning, existing methodologies st…
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
Numan Saeed, Shahad Hardan, Muhammad Ridzuan +3
Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing info…
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub +1
One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. Wh…
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network
Muhammad Ridzuan, Numan Saeed, Fadillah Adamsyah Maani +2
Survival analysis plays a crucial role in estimating the likelihood of future events for patients by modeling time-to-event data, particularly in healthcare settings where predicti…
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…
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
Nada Saadi, Numan Saeed, Mohammad Yaqub +1
Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these…
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.…
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky +1
Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing mechanisms among multiple entit…
On the Importance of Image Encoding in Automated Chest X-Ray Report Generation
Otabek Nazarov, Mohammad Yaqub, Karthik Nandakumar
Chest X-ray is one of the most popular medical imaging modalities due to its accessibility and effectiveness. However, there is a chronic shortage of well-trained radiologists who…
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub
We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and m…
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
Sayed Hashim, Muhammad Ali, Karthik Nandakumar +1
For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features…