Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops
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arXiv
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2025
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| author | Harmanani, Mohamed Jamzad, Amoon To, Minh Nguyen Nhat Wilson, Paul F. R. Guo, Zhuoxin Fooladgar, Fahimeh Sojoudi, Samira Gilany, Mahdi Chang, Silvia Black, Peter Leveridge, Michael Siemens, Robert Abolmaesumi, Purang Mousavi, Parvin |
| author_facet | Harmanani, Mohamed Jamzad, Amoon To, Minh Nguyen Nhat Wilson, Paul F. R. Guo, Zhuoxin Fooladgar, Fahimeh Sojoudi, Samira Gilany, Mahdi Chang, Silvia Black, Peter Leveridge, Michael Siemens, Robert Abolmaesumi, Purang Mousavi, Parvin |
| contents | Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel-level cancer annotations, introducing label noise. Current approaches often focus on limited regions of interest (ROIs), disregarding anatomical context necessary for accurate diagnosis. Foundation models can overcome this limitation by analyzing entire images to capture global spatial relationships; however, they still encounter challenges stemming from the weak labels associated with coarse pathology annotations in ultrasound data. We introduce Cinepro, a novel framework that strengthens foundation models' ability to localize PCa in ultrasound cineloops. Cinepro adapts robust training by integrating the proportion of cancer tissue reported by pathology in a biopsy core into its loss function to address label noise, providing a more nuanced supervision. Additionally, it leverages temporal data across multiple frames to apply robust augmentations, enhancing the model's ability to learn stable cancer-related features. Cinepro demonstrates superior performance on a multi-center prostate ultrasound dataset, achieving an AUROC of 77.1% and a balanced accuracy of 83.8%, surpassing current benchmarks. These findings underscore Cinepro's promise in advancing foundation models for weakly labeled ultrasound data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops Harmanani, Mohamed Jamzad, Amoon To, Minh Nguyen Nhat Wilson, Paul F. R. Guo, Zhuoxin Fooladgar, Fahimeh Sojoudi, Samira Gilany, Mahdi Chang, Silvia Black, Peter Leveridge, Michael Siemens, Robert Abolmaesumi, Purang Mousavi, Parvin Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel-level cancer annotations, introducing label noise. Current approaches often focus on limited regions of interest (ROIs), disregarding anatomical context necessary for accurate diagnosis. Foundation models can overcome this limitation by analyzing entire images to capture global spatial relationships; however, they still encounter challenges stemming from the weak labels associated with coarse pathology annotations in ultrasound data. We introduce Cinepro, a novel framework that strengthens foundation models' ability to localize PCa in ultrasound cineloops. Cinepro adapts robust training by integrating the proportion of cancer tissue reported by pathology in a biopsy core into its loss function to address label noise, providing a more nuanced supervision. Additionally, it leverages temporal data across multiple frames to apply robust augmentations, enhancing the model's ability to learn stable cancer-related features. Cinepro demonstrates superior performance on a multi-center prostate ultrasound dataset, achieving an AUROC of 77.1% and a balanced accuracy of 83.8%, surpassing current benchmarks. These findings underscore Cinepro's promise in advancing foundation models for weakly labeled ultrasound data. |
| title | Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2501.12331 |