Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913294873264128 |
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| author | Harmanani, Mohamed Wilson, Paul F. R. Fooladgar, Fahimeh Jamzad, Amoon Gilany, Mahdi To, Minh Nguyen Nhat Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin |
| author_facet | Harmanani, Mohamed Wilson, Paul F. R. Fooladgar, Fahimeh Jamzad, Amoon Gilany, Mahdi To, Minh Nguyen Nhat Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin |
| contents | PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) 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 CNN 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 evaluate 3 image transformers on ROI-scale cancer classification, then use the strongest model to tune a multi-scale classifier with MIL. 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 can improve performance of MIL, with a 77.9% AUROC, a sensitivity of 75.9%, and a specificity of 66.3%. CONCLUSION: Convolutional networks are better suited for modelling sparse datasets of prostate ultrasounds, producing more robust features than transformers in PCa detection. Multi-scale methods remain the best architecture for this task, with multi-objective learning presenting an effective way to improve performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_18233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data Harmanani, Mohamed Wilson, Paul F. R. Fooladgar, Fahimeh Jamzad, Amoon Gilany, Mahdi To, Minh Nguyen Nhat Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) 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 CNN 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 evaluate 3 image transformers on ROI-scale cancer classification, then use the strongest model to tune a multi-scale classifier with MIL. 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 can improve performance of MIL, with a 77.9% AUROC, a sensitivity of 75.9%, and a specificity of 66.3%. CONCLUSION: Convolutional networks are better suited for modelling sparse datasets of prostate ultrasounds, producing more robust features than transformers in PCa detection. Multi-scale methods remain the best architecture for this task, with multi-objective learning presenting an effective way to improve performance. |
| title | Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2403.18233 |