Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

Abstract

Background Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations. Methods A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes. Results The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. Conclusions The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.

Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach

“…The application of the CAD system has widened the diagnostic horizon in several other disease diagnoses, such as CSR [33], lung tumor [34], brain tumor [35], skin tumor [17], and prostate cancer [18]. The fundus images provide a clear picture of the eye's internal structure and are widely used for glaucoma diagnosis.…”

“…Computer-aided diagnostic (CAD) tools for automatically detecting glaucoma are common in clinical practice. The applications of machine learning and, most recently, deep learning (DL) algorithms [16][17][18][19] have increased the diagnostic accuracy of these automated tools for detecting glaucoma.…”

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management

“…T2 images in the sagittal, coronal, and axial planes, as well as DWI, ADC, and DCE images, were independently processed in separate 3D layers. The optimized model with knowledge encoding on training achieved several better classification results than the traditional architecture (AUC of 0.84 vs. AUC of 0.82) [32]. Sanyal et al described a two-stage convolutional neural network model of two U-Net networks.…”

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

“…Data from each modality are applied individually or combined with others. In 374 articles, im- age modality was present in 247, which were image data individually used in 129 articles [49], [51], [52], [54], [56], [57], [59]- [61], [63], [64] [103], [105], [106], [108], [110], [112]- [115], [117], [120]- [123], [128], [130], [134], [135], [137]- [149], [151], [152], [154], [155], [157], [158], [160], [162], [172]- [174], [176], [178], [191], [194]- [196], [198], [202], [203], [209], [210], [212], [214],…”