42 citations · 78 across the 7 of their papers we have counts for
10 papers
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
Michal Kazmierski, Benjamin Haibe-Kains
Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology. Machine learning (ML) offers a promising way to exp…
Evaluation of statistical approaches for association testing in noisy drug screening data
Petr Smirnov, Ian Smith, Zhaleh Safikhani +9
dentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to…
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Michal Kazmierski, Mattea Welch, Sejin Kim +12
Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider rang…
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
Sejin Kim, Michal Kazmierski, Benjamin Haibe-Kains
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been signific…
Learning across label confidence distributions using Filtered Transfer Learning
Seyed Ali Madani Tonekaboni, Andrew E. Brereton, Zhaleh Safikhani +3
Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve th…
CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar +19
Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease a…