Search Results for author: Benjamin Haibe-Kains

Found 9 papers, 2 papers with code

Lymph Node Graph Neural Networks for Cancer Metastasis Prediction

no code implementations3 Jun 2021 Michal Kazmierski, Benjamin Haibe-Kains

Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology.

Decision Making

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

no code implementations28 Jan 2021 Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Princess Margaret Head, Neck Cancer Group, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Andrew Hope, Scott Bratman, Benjamin Haibe-Kains

We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.

Survival Prediction

Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

1 code implementation10 Dec 2020 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.

Survival Prediction

Learning across label confidence distributions using Filtered Transfer Learning

no code implementations3 Jun 2020 Seyed Ali Madani Tonekaboni, Andrew E. Brereton, Zhaleh Safikhani, Andreas Windemuth, Benjamin Haibe-Kains, Stephen MacKinnon

In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets.

Transfer Learning

Reducing Adversarial Example Transferability Using Gradient Regularization

no code implementations16 Apr 2019 George Adam, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg

We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models.

Dr.VAE: Drug Response Variational Autoencoder

no code implementations26 Jun 2017 Ladislav Rampasek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg

We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction.

General Classification

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