Storkey, Amos

Mair, Grant

Fontanella, Alessandro

UK Research and Innovation (UKRI)

2025-11-06T14:19:02Z

2025-11-06T14:19:02Z

2025-11-06

This thesis explores deep learning (DL) techniques for analysing medical images, with a particular focus on brain CT and MRI. We begin by proposing a comprehensive semi-automatic pipeline to tackle the challenges of preparing and standardising a dataset of routinely-collected CT brain scans from the Third International Stroke Trial (IST-3) for DL analysis. Using these scans, we develop a convolutional neural network-based method to detect acute ischemic stroke (AIS) lesions and classify the affected brain side. To address the challenge of correctly classifying subtle lesions, we introduce the Adversarial Counterfactual Attention (ACAT) framework, which addresses the limitations of traditional CNNs in medical imaging tasks where only small parts of the image are informative. ACAT employs saliency maps to obtain soft spatial attention masks that modulate image features at different scales and increases the baseline classification accuracy of lesions in brain CT scans from 71.39% to 72.55 % and of COVID-19 related findings in lung CT scans from 67.71 % to 70.84%. We investigate the best way to generate the saliency maps employed in our architecture and propose a way to obtain them from adversarially generated counterfactual images. They are able to isolate the area of interest in brain and lung CT scans without using any manual annotations. In the task of localising the lesion location out of 6 possible regions, they obtain a score of 65.05% on brain CT scans, improving the score of 61.29 % obtained with the best competing method. ACAT is able to identify where an image should be modified, but not exactly how to modify it to obtain a credible counterfactual. Therefore, we present a weakly supervised method for generating healthy counterfactuals of diseased images and obtaining pixel-wise anomaly maps. This approach combines Denoising Diffusion Probabilistic Models (DDPM) and Denoising Diffusion Implicit Models (DDIM) in a novel way to perform targeted modifications to pathological areas while preserving the rest of the image. The process begins with a saliency map obtained through ACAT, which approximately covers the pathological areas. A diffusion model trained on healthy samples is then employed, using DDPM to modify lesion-affected areas within the saliency map, while DDIM ensures accurate reconstruction of normal anatomy outside these regions. The two parts are also fused at each timestep, to guarantee the generation of a sample with a coherent appearance and a seamless transition between edited and unedited parts. We compare our approach with alternative weakly supervised methods on the task of brain lesion segmentation, achieving the highest mean Dice and IoU scores among the models considered.

https://hdl.handle.net/1842/44133

http://dx.doi.org/10.7488/era/6657

en

The University of Edinburgh

Alessandro Fontanella, Emma Pead, Tom MacGillivray, Miguel O Bernabeu, and Amos Storkey. Classification with a domain shift in medical imaging. Medical Imaging Meets NeurIPS Workshop, 2020

Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair, Emanuele Trucco, and Amos Storkey. ACAT: Adversarial counterfactual attention for classification and detection in medical imaging. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 10153–10169. PMLR, 2023

Alessandro Fontanella,Wenwen Li, Grant Mair, Antreas Antoniou, Eleanor Platt, Paul Armitage, Emanuele Trucco, Joanna M Wardlaw, and Amos Storkey. Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT. Stroke and Vascular Neurology, 2024a

Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, and Amos Storkey. Diffusion models for counterfactual generation and anomaly detection in brain images. IEEE Transactions on Medical Imaging, 2024b

C​r​e​a​t​i​v​e ​C​o​m​m​o​n​s: ​A​t​t​r​i​b​u​t​i​o​n 4.0 International (​C​C-​B​Y 4.0)

https://creativecommons.org/licenses/by/4.0/

Counterfactual examples

Attention

Anomaly maps

Diffusion models

Medical image analysis

Region-based deep learning methods to enhance subtle lesion detection

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy