Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data

no code implementations • 28 May 2023 • Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov, Kun Zhang

The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data.

Causal Discovery

Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation

no code implementations • 13 Mar 2023 • Shahad Hardan, Hussain Alasmawi, Xiangjian Hou, Mohammad Yaqub

In this work, we propose a weakly supervised machine learning approach that learns from only ceT1 scans and adapts to segment two structures from hrT2 scans: the VS and the cochlea from the crossMoDA dataset.

Unsupervised Domain Adaptation

Energy Pricing in P2P Energy Systems Using Reinforcement Learning

1 code implementation • 24 Oct 2022 • Nicolas Avila, Shahad Hardan, Elnura Zhalieva, Moayad Aloqaily, Mohsen Guizani

The microgrid considers consumers, prosumers, the service provider, and a community battery.

reinforcement-learning Reinforcement Learning (RL)

Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?

no code implementations • 16 Jan 2022 • Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub

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 learning models for this purpose.