Abstract
In recent years, deep learning-based methods have been extensively developed to diagnose Prostate Cancer (PCa) with bi-parametric Magnetic Resonance Imaging (bpMRI) images. Due to the vague characteristic of PCa bpMRI images, the spacial lesion annotations and grading annotations inevitably contain some noises, which has a serious impact on the performance of deep learning models for the diagnosis of PCa. Furthermore, the similar background features of PCa bpMRI image also disturb the prediction performance of deep learning models. In this paper, we propose a two-branch Noise-Resistant Distillation Network (NRD-Net) for the accurate diagnosis of PCa with bpMRI images. Firstly, the influence of irrelevant background on the classification can be reduced by segmenting the labeled constrained classification response maps. Then a novel confidence-based binarization segmentation scheme and a multi-branch online distillation classification scheme are proposed to reduce the spatial and grading noises simultaneously. Extensive experiments are conducted on a private dataset and the public PROSTATEx-2 dataset. For the private dataset, the proposed network obtains the best performance for GG prediction, achieving a mean quadratic weighted Kappa of 0.5115 and a mean positive predictive value (PPV) of 0.9506. For the public dataset, the proposed method achieves state-of-the-art results of 0.5058 Kappa and 0.9473 PPV.
Similar content being viewed by others
References
Sanjay K, Rajesh S, Shalie M, Upender M, Manoj M (2018) Prostate cancer health disparities: An immuno-biological perspective. Cancer Letters 414:153–165
Mamta J, Sumindar KS, Jatin G, Poojita G, Niharika T, Aviral S, Manan M, Prashant J (2021) Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer. Multimed Tools Appl 80:29199–29249
Garg Gaurav, Juneja Mamta (2021) Particle swarm optimization based segmentation of Cancer in multi-parametric prostate MRI. Multimed Tools Appl 80:30557–30580
Zhi QT, Xiao JL, Zhang C, Yao YZ, Hong CF, Zhong YL, Ce L, S, D, (2021) Interactive prostate MR image segmentation based on ConvLSTMs and GGNN. Neurocomputing 438:84–93
Nga B, Yha B, Ega C, Eba B, Meb D, Cmmb D, Dcba B (2019) Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Medical Image Analysis 58:101558–101558
Cao RM, Bajgiran AM, Mirak SA, Shakeri S, Zhong XR, Enzmann D, Raman S, Sung K (2019) Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Trans Med Imaging. 11(38):2496–2506
Abraham B, Nair MS (2019) Automated grading of prostate cancer using convolutional neural network and ordinal class classifier. Informat Med Unlocked 17:100256
Abraham B, Nair MS (2019) Computer-aided grading of prostate cancer from MRI images using Convolutional Neural Networks. J Intell Fuzzy Syst 36:1–10
Zhi WW, Chao YL, Dan PC, Liang W, Xin Y, Kwang TC (2018) Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network. IEEE Trans Med Imaging 37:1127–1139
Hou QY, Xu MZ (2020) Synthesis of Prostate MR Images for Classification Using Capsule Network-Based GAN Model. Sensors 20:5736
Liu Z, Jiang W, Lee K, Lo Y, Ng Y, Dou Q, Vardhanabhuti V, Kwok K (2019) A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network. Artif Intell Radiat Ther 43–51
Betrouni N, Vermandel M, Pasquier D, Rousseau J (2007) Ultrasound image guided patient setup for prostate cancer conformal radiotherapy. Pattern Recog Lett 28(13):1808–1817
Puech P, Huglo D, Petyt G, Lemaitre L, Villers A (2009) Imaging of organ-confined prostate cancer: functional ultrasound, MRI and PET/computed tomography. Curr Opin Urol 19(2):168–176
Micheal YC, Maria AW, Prokar D, Nicholas JR (2020) Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med 9(19):7172–7182
Hu J, Shen A, Qiao X, Zhou Z, Qian X, Zheng Y, Bao J, Wang X, Dai Y (2022) Dual attention guided multi-scale neural network trained with curriculum learning for noninvasive prediction of Gleason grade groups from MRI. Med Phys
Lin ZLC, Tan JWB, Ranasinghe W, Shahbaz S, McCahy P (2015) Correlation between trans-rectal ultrasound guided (TRUS) biopsies reporting standards and multi-disciplinary team discussions, in the management of prostate cancer. Pathology 47:S68
Liang G, Kele X, Huaimin W, Yuxing P (2020) Multi-representation knowledge distillation for audio classification. Multimed Tools Appl 81:5089–5112
Hu JC, Feng ZL, Mao YN, Lei J, Yu D, Song ML (2021) A Location Constrained Dual-Branch Network for Reliable Diagnosis of Jaw Tumors and Cysts. Medical Image Computing and Computer Assisted Intervention
Ge Y, Choi CL, Zhang X, Zhao P, Zhu F, Zhao R, Li H (2021) Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification. ArXiv
Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2010) elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE Trans Med Imaging 29:196–205
Sim J, Wright CC (2005) The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Therapy 85(3):257–268
Ishioka J, Matsuoka Y, Uehara S, Yasuda Y, Kijima T, Yoshida S, Yokoyama M, Saito K, Kihara K, Numao N (2018) Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. Bju International 122(3):411–417
Seah J, Tang JSN, Kitchen A (2017) Detection of prostate cancer on multiparametric MRI. Med Imaging
Adamantios Z, Nikolaos P, Anastasios T (2021) Improving knowledge distillation using unified ensembles of specialized teachers. Pattern Recog Lett 146:215–221
Fausto M, Nassir N, Seyed AA (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision 565–571
Duchi JC, Hazan E, Singer Y (2011) Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J Mach Learn Res 2121–2159
Jit YL, Kian ML, Shih YO, Chin PL (2021) Efficient-PrototypicalNet with self knowledge distillation for few-shot learning. Neurocomputing 459:327–337
Zhu JL, Deng F, Zhao JC, Jie C (2022) Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection. Pattern Recognit 131:108897
Qian W, He ZQ, Chen C, Peng SL (2022) Partner learning: A comprehensive knowledge transfer for vehicle re-identification. Neurocomputing 480:89–98
Leonardo R, Changhee H, Yudai N, Jin Z, Ryuichiro H, Carmelo M, Andrea T, Marco SN, Claudio F, Daniela B, Maria CG, Salvatore V, Giancarlo M, Hideki N, Paolo C (2019) USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365:31–43
Sharmila D, Karthik A, Armstrong IS, Panithaya C, Dpe Gordon, Einstein AJ, Gropler RJ, Holly TA, Mahmarian JJ, Mi-Ae P (2018) Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging Guidelines: Instrumentation, Acquisition, Processing, and Interpretation. J Nuclear Cardiol 25:1–63
Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B, Yang G (2018) Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging 48:1570–1577
Aldoj N, Lukas S, Dewey M, Penzkofer T (2019) Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network. Eur Radiol 30:1243–1253
Tong CC, Ying S, Nicolas P, Sim HO (2012) A general strategy for anisotropic diffusion in MR image denoising and enhancement. Magn Reson Imaging 30:1381–1393
Armato SG, Huisman H, Drukker K, Hadjiiski L, Kirby JS, Petrick N, Redmond G, Giger ML, Cha K, Mamonov A (2018) PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging 5:044501
Wang Y, Wang M (2020) Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network. Phys Med 80:92–100
Abraham B, Nair MS (2018) Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder. Comput Med Imaging Graph 69:60–68
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier HKH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18:203–211
Zia K, Norashikin Y, Khaled A, Fabrice M (2020) Segmentation of Prostate in MRI Images Using Depth Separable Convolution Operations. 14th International Conference on Interfaces and Human Computer Interaction 132–141
Ekam SC, Aarya P, Ayush G, Archana P, Deepa BG (2021) Unet based Xception Model for Prostate Cancer Segmentation from MRI Images. Multimed Tools Appl 81:37333–37349
Tian ZQ, Liu LZ, Zhang ZF, Fei BW (2018) PSNet: prostate segmentation on MRI based on a convolutional neural network. J Med Imaging 5
Rani JS (2013) Noise Removal In Medical Images Using Filters. Int J Eng Res Technol 2
Wang XH, Chen HJ, Wan Q, Li YF, Cai NX, Li XC, Peng YH (2020) Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition. Med Biol Eng Comput 58:2095–2105
Vaishali S, Kishan RK, Subba RGV (2015) A review on noise reduction methods for brain MRI images. International Conference on Signal Processing and Communication Engineering Systems 363–365
Liu FB, Tian Y, Filipe RC, Vasileios B, Ian DR, Carneiro G (2021) Noisy Label Learning for Large-scale Medical Image Classification. ArXiv
Lie J, Xin W, Lin W, Dwarikanath M, Xin Z, Mehrtash TH, Tom D, Liu TL, Ge ZY (2022) Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation. IEEE Trans Med Imaging 41:1533–1546
Yair D, Hayit G, Jacob G (2018) Training a neural network based on unreliable human annotation of medical images. IEEE 15th International Symposium on Biomedical Imaging 39–42
Bednarova S, Lindenberg ML, Vinsensia M, Zuiani C, Choyke PL, Turkbey BI, (2017) Positron emission tomography (PET) in primary prostate cancer staging and risk assessment. Transl Androl Urol 6:413–423
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv
Dai Z, Jambor I, Taimen P, Pantelic M, Elshaikh M (2023) Prostate cancer detection and segmentation on MRI using non-local mask R-CNN with histopathological ground truth. Med Phys 1–16
Vente C, Vos P, Hosseinzadeh M, Pluim J, Veta M (2020) Deep learning regression for prostate cancer detection and grading in bi-parametric MRI. IEEE Trans Biomed Eng 68(2):374–383
Shen A, Hu J, Jin P, Zhou Z (2022) Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI. J Shanghai Jiaotong Univ (Science)
Hu J, Shen A, Jin P, Zhou Z (2023) Dual attention-guided multiscale neural network trained with curriculum learning for noninvasive grading of prostate cancer. Med Phys 50(4):2279–2289
Chinmay C, Amit K, Joel J (2022) Novel Enhanced-Grey Wolf Optimization hybrid machine learning technique for biomedical data computation. Comput Electr Eng 99:107778
Amit K, Chinmay C (2022) Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System. Wirel Pers Commun 127:1615–1631
Chinmay C, Amit K (2022) Real-Time Cloud-Based Patient-Centric Monitoring Using Computational Health Systems. IEEE Trans Comput Soc Syst 99:1–11
Amit K, Chinmay C, Wilson J (2021) Reinforcement learning for medical information processing over heterogeneous networks. Multimed Tools Appl 80:23983–24004
Juan X, Stanley A, Xinyu C (2023) Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning. Measurement 214:112764
Acknowledgements
This work is supported in part by Zhejiang Province Science and Technology Project for Public Welfare (LGF21F020020), and Suzhou Municipal Health and Family Planning Commission’s Key Diseases Diagnosis and Treatment Program (LCZX202001).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Du, X., Shen, A., Wang, X. et al. NRD-Net: a noise-resistant distillation network for accurate diagnosis of prostate cancer with bi-parametric MRI images. Multimed Tools Appl 83, 33597–33614 (2024). https://doi.org/10.1007/s11042-023-16712-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16712-z