Search Results for author: Chris McIntosh

Found 6 papers, 0 papers with code

MEDBind: Unifying Language and Multimodal Medical Data Embeddings

no code implementations19 Mar 2024 Yuan Gao, SangWook Kim, David E Austin, Chris McIntosh

Medical vision-language pretraining models (VLPM) have achieved remarkable progress in fusing chest X-rays (CXR) with clinical texts, introducing image-text data binding approaches that enable zero-shot learning and downstream clinical tasks.

Language Modelling Large Language Model +2

Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications

no code implementations7 Sep 2023 SangWook Kim, Thomas G. Purdie, Chris McIntosh

Multi-task learning (MTL) is a powerful approach in deep learning that leverages the information from multiple tasks during training to improve model performance.

COVID-19 Diagnosis Lesion Segmentation +3

Non-invasive Liver Fibrosis Screening on CT Images using Radiomics

no code implementations25 Nov 2022 Jay J. Yoo, Khashayar Namdar, Sean Carey, Sandra E. Fischer, Chris McIntosh, Farzad Khalvati, Patrik Rogalla

The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1. 5 (AUC, 0. 7833; 95% CI: 0. 7821, 0. 7845), (sensitivity, 0. 9091; 95% CI: 0. 9091, 0. 9091).

feature selection regression

Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance

no code implementations7 Mar 2022 Aly Khalifa, Jeff Winter, Inmaculada Navarro, Chris McIntosh, Thomas G. Purdie

Significance: We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed.

Computed Tomography (CT) Domain Adaptation

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.

BIG-bench Machine Learning Survival Prediction

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