Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths with rising incidence. Since the survival rate of CRC is correlated with the cancer stage at diagnosis, timely detection and adequate treatment strategies are of utmost importance. Technical innovations such as machine learning (ML) and its application in endoscopy show promising results, but the trust of medical doctors in ML is lacking and the ‘black box’ nature complicates the understanding of such systems in clinical practice. In contrast to CT and MRI, image quality is a limiting factor in especially endoscopic imaging, as it is very operator dependent. However, the influence of image quality on convolutional (deep) neural networks (CNNs) is insufficiently studied in relation to clinical practice and the usage of medical image data for computer-aided detection and diagnosis (CADx) systems. This paper explores the influence of degraded image quality on the performance of CNNs applied to colorectal polyp (CRP) characterization. Five commonly used CNN architectures, from simple to more complex, are employed with a custom classification head for common CRP characterization. To degrade the quality of images, distortions such as noise, blur, and contrast changes are imposed on the data and their influence on the performance degradation is studied for the mentioned CNN architectures. A large prospectively collected in vivo data set, gathered from four Dutch, both academic and community, hospitals is employed. Results for CRP characterization show that promising CNN-based methods are rather susceptible to noise and blur distortions but reasonably resilient to changes in contrast. This implies that image quality needs monitoring and control prior to directly using image data in CNN models, in order to gain trustworthy use of deep learning (DL) models in a clinical setting. We propose that incorporating an image quality indicator in CADx systems will lead to better acceptance of such systems, and is necessary for the safe implementation of DL applications in clinical practice.

Colorectal polyps (CRPs) are potential precursors of colorectal cancer (CRC), one of the most common types of cancer worldwide. Computer-Aided Diagnosis (CADx) systems can play a crucial role as a second opinion for endoscopists in characterizing CRPs and contribute to the diagnostic performance of colonoscopies. Despite their potential, deep neural network-based systems often tend to overestimate the confidence about their decisions and provide predictive probabilities that are poorly related to their classification accuracy. Quantifying uncertainty of such supportive systems is crucial for optimal clinical workflow integration and physician's acceptance. Thus, a trustworthy CADx system is expected to provide accurate and well-calibrated classification confidence. Transfer learning from either natural image datasets, such as ImageNet, or other datasets with similar modalities, has been widely used for improving the accuracy of deep learning-based systems in medical image classification. In this paper, we study the impact of domain-specific pretraining on the calibration and the overall performance of a CADx system for CRP characterization. We evaluate our hypothesis on a fully deterministic and a hybrid Bayesian version of each approach using a generic ResNet50 architecture. Experimental results demonstrate the effectiveness of domain-specific pretraining in achieving a higher overall characterization AUC. Additionally, the in-domain and out-of-domain pretrained models portray similar calibration error rates, however, their corresponding hybrid Bayesian models offer higher robustness with improved calibration performance. A hybrid Bayesian version of a domain-specific pretraining approach has shown to significantly improve the accuracy and reliability of CADx systems used for CRP characterization and similar positive effects may be expected for other medical imaging applications.

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