다중 파라미터 MR 영상 융합과 자기지도학습 모델의 추가 학습을 통한 전립선암 악성도 예측 성능 개선
Enhanced Performance of Prostate Cancer Aggressiveness Prediction through Fusion of Multi-parametric MR Images and Additional Training of a Self-supervised Learning Model
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초록
본 연구는 전립선암 악성도 예측 성능을 개선하기 위해 다중 파라미터 MR 영상들을 다양한 방식으로 융합하고, 자기 지도학습 모델을 전립선암 악성도 예측에 사용할 데이터로 추가 훈련하는 방법을 제안한다. 실험 결과, 다중 파라미터 MR 영상들을 사용할 때 전립선암 악성도 예측 성능이 단일 파라미터 MR 영상만을 사용할 때보다 개선되었다. 다중 파 라미터 MR 영상들을 이용한 과반수 투표 방식의 예측 융합이 가장 높은 성능을 보였다. 또한 전립선암 악성도 예측에 사용할 데이터셋으로 자기지도학습을 추가 수행한 모델의 성능이 모든 단일 및 다중 파라미터 MR 영상의 실험 결과에 대해 정확도, 민감도, 음성예측도, AUROC 점수가 평균적으로 0.8%, 4.49%, 1.77%, 0.02의 향상된 성능을 보였다.
Abstract
This study proposes a method to improve cancer aggressiveness prediction performance by fusing multi-parametric MR images in various ways and additionally training a self-supervised learning model with data used for prostate cancer aggressiveness prediction. Experimental results showed that the prediction performance of prostate cancer aggressiveness improved when using multi-parametric MR images compared to using single-parametric MR image. The prediction fusion method of majority voting method with multi- parametric MR images showed the highest performance. Furthermore, the model with additional self-supervised learning on the dataset used for prostate cancer aggressiveness prediction showed an average performance improvement of 0.8% in accuracy, 4.49% in sensitivity, 1.77% in negative predictive value, and 0.02 in AUROC score across all single and multi-parametric MR image experiments.
참고문헌 (18)
- F. Bray, "Global Cancer Statistics 2018 : gLOBOCANestimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries", CA Cancer J Clin, 68(6), pp.394~424 (2018)
- F. H. Schröder, "Screening and Prostate-Cancer Mortalityin a Randomized European Study", The New England Journal of Medicine, 360(13), pp.1320~1328 (2009)
- J. I. Epstein, "The 2014 International Society of Urological Pathology(ISUP)Consensus Conference on Gleason Grading of Prostatic Carcinoma", The American Journal of Surgical Pathology, 40(2), pp.244~252 (2016)
- BoSung Shin, "Clinical Features of Bacteremia Caused by Ciprofloxacin-Resistant Bacteria after Transrectal Ultrasound-Guided Prostate Biopsy", Urogenital Tract Infection, 6(1), pp.61~66 (2011)
- N. Aldoj, "Semi-automatic Classification of Prostate Cancer on Multi-Parametric MR Imaging using a Multi-Channel 3D Convolutional Neural Network", European radiology, 30(2), pp.1243~1253 (2020)
- T. Sanford, "Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation : A Development Study", Journal of Magnetic Resonance Imaging, 52(5), pp.1499~1507 (2020)
- Li Y, "MRI-based prostate cancer classification using 3D efficient capsule network" (2024), Med Phys
- S. Albelwi, "Survey on Self-Supervised Learning : Auxiliary Pretext Tasks and Contrastive Learning Methods in Imaging", Entropy, 24(4), pp.551~ (2022)
- Bochong Li, "Robust multi-modal prostate cancer classification via feature autoencoder and dual attention", Informatics in Medicine Unlocked, 30 (2022)
- ossi A, "Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI", IEEE Trans Med Imaging, 40(3), pp.986~995 (2021)
- Yejin Shin, "Improvement of Prostate Cancer Aggressiveness Prediction Performance Using a Self-Supervised Learning Model Fine-Turned on Similar Medical Images from Multi-Parametric MR Images", Journal of Korea Multimedia Society, 26(8), pp.995~1002 (2023)
- T. Chen, "A Simple Framework for Contrastive Learning of Visual Representations", International Conference on Machine Learning, 119, pp.1597~1607 (2020), PMLR
- K. He, "Deep Residual Learning for Image Recognition", IEEE Conference on Computer Vision and Pattern Recognition, pp.770~778 (2016)
- G. Litjens, "ProstateX Challenge Data", The Cancer Imaging Archive, ~ (2017)
- Kaplan, Bonnie, "Combining Qualitative and Quantitative Methods in Information Systems Research : A Case Study", MIS Quarterly, 12(4), pp.571~586 (1988)
- Kaplan, B, "Health Informatics", pp.30~55 (2006), Springer
- Van der Maaten, Laurens, "Visualizing data using t-SNE", Journal of machine learning research, 9(11) (2008)
- Peter J. Rousseeuw, "Silhouettes : A graphical aid to the interpretation and validation of cluster analysis", Journal of Computational and Applied Mathematics, 20, pp.53~65 (1987)