6 Result(s)
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Ainkaran Santhirasekaram
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Article
Open AccessRobust prostate disease classification using transformers with discrete representations
Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free...
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Chapter and Conference Paper
Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation
Deep learning based medical image segmentation models need to be robust to domain shifts and image distortion for the safe translation of these models into clinical practice. The most popular methods for impro...
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Article
Open AccessIs tumour volume an independent predictor of outcome after radical prostatectomy for high-risk prostate cancer?
Preoperative PSA, ISUP grade group (GG), prostate examination and multiparametric MRI (mpMRI) form the basis of prostate cancer staging. Unlike other solid organ tumours, tumour volume (TV) is not routinely us...
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Chapter and Conference Paper
A Sheaf Theoretic Perspective for Robust Prostate Segmentation
Deep learning based methods have become the most popular approach for prostate segmentation in MRI. However, domain variations due to the complex acquisition process result in textural differences as well as i...
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Chapter and Conference Paper
Vector Quantisation for Robust Segmentation
The reliability of segmentation models in the medical domain depends on the model’s robustness to perturbations in the input space. Robustness is a particular challenge in medical imaging exhibiting various so...
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Chapter and Conference Paper
Multi-scale Hybrid Transformer Networks: Application to Prostate Disease Classification
Automated disease classification could significantly improve the accuracy of prostate cancer diagnosis on MRI, which is a difficult task even for trained experts. Convolutional neural networks (CNNs) have show...
