Ms. Nikoo Dehghani Profile

Nikoo Dehghani

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Publications (3)

Proceedings Article | 7 April 2023 Presentation + Paper

How does image quality affect computer-aided diagnosis of colorectal polyps?

Thom Scheeve, Nikoo Dehghani, Quirine E. van der Zander, Ayla Thijssen, Ramon-Michel Schreuder, Ad A. Masclee, Erik Schoon, Fons van der Sommen, Peter H. de With

Proceedings Volume 12465, 124650S (2023) https://doi.org/10.1117/12.2654167

KEYWORDS: Image quality, Endoscopy, Polyps, Cancer, Colorectal cancer, Clinical practice, Deep learning, Distortion

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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.

Proceedings Article | 7 April 2023 Presentation + Paper

Effect of domain-specific self-supervised pretraining on predictive uncertainty for colorectal polyp characterization

N. Dehghani, T. Scheeve, T. G. Boers, Q. E. van der Zander, A. Thijssen, R. Schreuder, A. A. Masclee, E. Schoon, F. van der Sommen, P. H. de With

Proceedings Volume 12465, 124651G (2023) https://doi.org/10.1117/12.2653848

KEYWORDS: Calibration, Polyps, Performance modeling, Computer aided detection, Reliability, Medical imaging, Endoscopy, Network architectures, Image classification

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Proceedings Article | 4 April 2022 Poster + Paper

Colorectal polyp classification using confidence-calibrated convolutional neural networks

Koen Kusters, Thom Scheeve, Nikoo Dehghani, Quirine E. van der Zander, Ramon-Michel Schreuder, Ad A. Masclee, Erik Schoon, Fons van der Sommen, Peter H. de With

Proceedings Volume 12033, 120331P (2022) https://doi.org/10.1117/12.2606801

KEYWORDS: Calibration, Reliability, Error control coding, Computer aided diagnosis and therapy, Convolutional neural networks, Systems modeling, Electrochemical etching, In vivo imaging, Endoscopy, Cancer

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Computer-Aided Diagnosis (CADx) systems for in-vivo characterization of Colorectal Polyps (CRPs) which are precursor lesions of Colorectal Cancer (CRC), can assist clinicians with diagnosis and better informed decisionmaking during colonoscopy procedures. Current deep learning-based state-of-the-art solutions achieve a high classification performance, but lack measures to increase the reliability of such systems. In this paper, the reliability of a Convolutional Neural Network (CNN) for characterization of CRPs is specifically addressed by confidence calibration. Well-calibrated models produce classification-confidence scores that reflect the actual correctness likelihood of the model, thereby supporting reliable predictions by trustworthy and informative confidence scores. Two recently proposed trainable calibration methods are explored for CRP classification to calibrate the confidence of the proposed CNN. We show that the confidence-calibration error can be decreased by 33.86% (−0.01648 ± 0.01085), 48.33% (−0.04415 ± 0.01731), 50.57% (−0.11423 ± 0.00680), 61.68% (−0.01553 ± 0.00204) and 48.27% (−0.22074 ± 0.08652) for the Expected Calibration Error (ECE), Average Calibration Error (ACE), Maximum Calibration Error (MCE), Over-Confidence Error (OE) and Cumulative Calibration Error (CUMU), respectively. Moreover, the absolute difference between the average entropy and the expected entropy was considerably reduced by 32.00% (−0.04374 ± 0.01238) on average. Furthermore, even a slightly improved classification performance is observed, compared to the uncalibrated equivalent. The obtained results show that the proposed model for CRP classification with confidence calibration produces better calibrated predictions without sacrificing classification performance. This work shows promising points of engagement towards obtaining reliable and well-calibrated CADx systems for in-vivo polyp characterization, to assist clinicians during colonoscopy procedures.

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