Mr. Paul F. R. Wilson Profile

Paul F. R. Wilson

at Queen's Univ.

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Publications (1)

Proceedings Article | 29 March 2024 Presentation + Paper

Benchmarking image transformers for prostate cancer detection from ultrasound data

Mohamed Harmanani, Paul F. Wilson, Fahimeh Fooladgar, Amoon Jamzad, Mahdi Gilany, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

Proceedings Volume 12928, 1292815 (2024) https://doi.org/10.1117/12.3006049

KEYWORDS: Transformers, Ultrasonography, Biopsy, Cancer detection, Prostate cancer

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PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional neural networks (CNN) to detect cancer in small regions of interest (ROI) along a needle trace region. However, this approach suffers from weak labelling, since the ground-truth histopathology labels do not describe the properties of individual ROIs. Recently, multi-scale approaches have sought to mitigate this issue by combining the context awareness of transformers with a convolutional feature extractor to detect cancer from multiple ROIs using multiple-instance learning (MIL). In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification. We also design a novel multi-objective learning strategy that combines both ROI and core predictions to further mitigate label noise. METHODS: We use a dataset of 6607 prostate biopsy cores extracted from 693 patients at 5 distinct clinical centers. We evaluate 3 image transformers on ROI-scale cancer classification, then use the strongest model to tune a multi-scale classifier with MIL, wherein another transformer is fine-tuned on top of the existing model’s features. We train our MIL models using our novel multi-objective learning strategy and compare our results to existing baselines. RESULTS: We find that for both ROI-scale and multi-scale PCa detection, image transformer backbones lag behind their CNN counterparts. This deficit in performance is even more noticeable for larger models. When using multi-objective learning, we are able to improve the performance of MIL models, with a 77.9% AUROC, a sensitivity of 75.9%, and a specificity of 66.3%, a considerable improvement over the baseline. CONCLUSION: We conclude that convolutional networks are better suited for modelling sparse datasets of prostate ultrasounds, producing more robust features than their transformer counterparts in PCa detection. Multi-scale methods remain the best architecture for this task, with multi-objective learning presenting an effective way to improve performance.

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