Merge pull request #141 from nhsengland/aib-add-publication-8

add publication

docs/our_work/Publications.md

@@ -8,13 +8,23 @@ tags: ['PUBLICATIONS']
 
 List of pre-releases and publications connected to our work
 
+[8] [https://ieeexplore.ieee.org/document/10635870](https://ieeexplore.ieee.org/document/10635870)
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+**Medisure: Towards Assuring Machine Learning-Based Medical Image Classifiers Using Mixup Boundary Analysis**
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+Adam Byfield, **William Poulett**, **Ben Wallace**, Anusha Jose, Shatakshi Tyagi, Smita Shembekar, Adnan Qayyum, Junaid Qadir, Muhammad Bilal
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+*Machine learning (ML) models are becoming integral in healthcare technologies, necessitating formal assurance methods to ensure their safety, fairness, robustness, and trustworthiness. However, these models are inherently error-prone, posing risks to patient health and potentially causing irreparable harm when deployed in clinics. Traditional software assurance techniques, designed for fixed code, are not directly applicable to ML models, which adapt and learn from curated datasets during training. Thus, there is an urgent need to adapt established software assurance principles such as boundary testing with synthetic data. To bridge this gap and enable objective assessment of ML models in real-world clinical settings, we propose Mix-Up Boundary Analysis (MUBA), a novel technique facilitating the evaluation of image classifiers in terms of prediction fairness. We evaluated MUBA using brain tumour and breast cancer classification tasks and achieved promising results. This research underscores the importance of adapting traditional assurance principles to assess ML models, ultimately enhancing the safety and reliability of healthcare technologies. Our code is available at [https: //github.com/willpoulett/MUBA_pipeline](https: //github.com/willpoulett/MUBA_pipeline).*
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 [7] [https://publichealth.jmir.org/2024/1/e46485](https://publichealth.jmir.org/2024/1/e46485)
 
 **The Use of Online Consultation Systems or Remote Consulting in England Characterized Through the Primary Care Health Records of 53 Million People in the OpenSAFELY Platform: Retrospective Cohort Study**
 
 **Martina Fonseca**, Brian MacKenna,  Amir Mehrkar, The OpenSAFELY Collaborative, Caroline E Walters, George Hickman,  **Jonathan Pearson**,  Louis Fisher,  Peter Inglesby,  Seb Bacon,  Simon Davy, William Hulme,  Ben Goldacre,  Ofra Koffman,  Minal Bakhai
 
-*We aimed to explore general practice coding activity associated with the use of Online Consultations (OC) systems in terms of trends, COVID-19 effect, variation, and quality.  The OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020.  We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes. *
+*We aimed to explore general practice coding activity associated with the use of Online Consultations (OC) systems in terms of trends, COVID-19 effect, variation, and quality.  The OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020.  We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes.*
 
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