Ben Glocker | Professor in Machine Learning for Imaging

Imperial Profile | dblp | Google Scholar | PubMed | arXiv | CV | Twitter | YouTube

I am Professor in Machine Learning for Imaging and Kheiron Medical Technologies / RAEng Research Chair in Safe Deployment of Medical Imaging AI. I am co-leading the Biomedical Image Analysis Group, lead the HeartFlow-Imperial Research Team and I am Head of ML Research at Kheiron.

My research is at the intersection of medical imaging and artificial intelligence aiming to build safe and ethical computational tools for improving image-based detection and diagnosis of disease.

^{Keynote at CHIL 2023}
Safe deployment of medical imaging AI

^{Opinion piece}
In AI development, equality starts with a mandate for fairness and inclusivity

^{Research article}
Roschewitz et al. (2023) Automatic correction of performance drift under acquisition shift in medical image classification

^{Research article}
Glocker et al. (2023) Risk of Bias in Chest Radiography Deep Learning Foundation Models

^{Research article}
Glocker et al. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models

^Commentary
Bernhardt, Jones, Glocker (2022) Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

^{Research article}
Bernhardt et al. (2022) Active label cleaning for improved dataset quality under resource constraints

^{Research article}
Sinclair et al. (2022) Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks

Recent Papers

Imperial Profile | dblp | Google Scholar | PubMed | arXiv | CV | Twitter | YouTube

^{Keynote at CHIL 2023}
Safe deployment of medical imaging AI

^{Opinion piece}
In AI development, equality starts with a mandate for fairness and inclusivity

^{Research article}
Roschewitz et al. (2023) Automatic correction of performance drift under acquisition shift in medical image classification

^{Research article}
Glocker et al. (2023) Risk of Bias in Chest Radiography Deep Learning Foundation Models

^{Research article}
Glocker et al. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models

^Commentary
Bernhardt, Jones, Glocker (2022) Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

^{Research article}
Bernhardt et al. (2022) Active label cleaning for improved dataset quality under resource constraints

^{Research article}
Sinclair et al. (2022) Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks

^{Research article}
Castro et al. (2020) Causality Matters in Medical Imaging

^{Research paper}
Pawlowski et al. (2020) Deep Structural Causal Models for Tractable Counterfactual Inference

Can you beat our AI?

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Recent Papers

Imperial Profile | dblp | Google Scholar | PubMed | arXiv | CV | Twitter | YouTube

Keynote at CHIL 2023Safe deployment of medical imaging AI

Opinion pieceIn AI development, equality starts with a mandate for fairness and inclusivity

Research articleRoschewitz et al. (2023) Automatic correction of performance drift under acquisition shift in medical image classification

Research articleGlocker et al. (2023) Risk of Bias in Chest Radiography Deep Learning Foundation Models

Research articleGlocker et al. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models

CommentaryBernhardt, Jones, Glocker (2022) Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

Research articleBernhardt et al. (2022) Active label cleaning for improved dataset quality under resource constraints

Research articleSinclair et al. (2022) Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks

Research articleCastro et al. (2020) Causality Matters in Medical Imaging

Research paperPawlowski et al. (2020) Deep Structural Causal Models for Tractable Counterfactual Inference

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Can you beat our AI?

Events

Software

Datasets

Contact

^{Keynote at CHIL 2023}
Safe deployment of medical imaging AI

^{Opinion piece}
In AI development, equality starts with a mandate for fairness and inclusivity

^{Research article}
Roschewitz et al. (2023) Automatic correction of performance drift under acquisition shift in medical image classification

^{Research article}
Glocker et al. (2023) Risk of Bias in Chest Radiography Deep Learning Foundation Models

^{Research article}
Glocker et al. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models

^Commentary
Bernhardt, Jones, Glocker (2022) Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

^{Research article}
Bernhardt et al. (2022) Active label cleaning for improved dataset quality under resource constraints

^{Research article}
Sinclair et al. (2022) Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks

^{Research article}
Castro et al. (2020) Causality Matters in Medical Imaging

^{Research paper}
Pawlowski et al. (2020) Deep Structural Causal Models for Tractable Counterfactual Inference