PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation
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2024
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| author | Magoulianitis, Vasileios Yang, Jiaxin Yang, Yijing Xue, Jintang Kaneko, Masatomo Cacciamani, Giovanni Abreu, Andre Duddalwar, Vinay Kuo, C. -C. Jay Gill, Inderbir S. Nikias, Chrysostomos |
| author_facet | Magoulianitis, Vasileios Yang, Jiaxin Yang, Yijing Xue, Jintang Kaneko, Masatomo Cacciamani, Giovanni Abreu, Andre Duddalwar, Vinay Kuo, C. -C. Jay Gill, Inderbir S. Nikias, Chrysostomos |
| contents | Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation Magoulianitis, Vasileios Yang, Jiaxin Yang, Yijing Xue, Jintang Kaneko, Masatomo Cacciamani, Giovanni Abreu, Andre Duddalwar, Vinay Kuo, C. -C. Jay Gill, Inderbir S. Nikias, Chrysostomos Image and Video Processing Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity. |
| title | PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2403.15969 |