A Real-Time Polyp Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks

Krenzer, Adrian; Banck, Michael; Makowski, Kevin; Hekalo, A.; Fitting, Daniel; Troya, Joel; Sudarevic, Boban; Wg, Zoller; Hann, Alexander; Puppe, Frank

doi:10.21203/rs.3.rs-1310139/v1

Cited by 5 publications

(6 citation statements)

References 57 publications

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“…Table 9 shows that our approach using a transformer architecture outperforms the two other CNN approaches in nearly all metrics. Especially on the harder-to-classify EndoData [9]. The improvement from BiT-R152x4 to our model shows an accuracy of 76.31% to 87.42 %.…”

Section: Discussionmentioning

confidence: 76%

“…Table 9 Test results of each model on two different test data sets, the SUN Colonoscopy Video data set and our own data set (EndoData) [9]. All values are given in %.…”

Section: Discussionmentioning

confidence: 99%

“…For the training and evaluation of the Paris classification system, we used two data sets. The second data set is EndoData this was created by us at the University clinic of Würzbug [9]. In the next section the proccess of the data creation will be outlined briefly.…”

Section: Parismentioning

confidence: 99%

“…In addition, the expert annotates the Paris and, if possible, the NICE classification [5], the size of the polyp and its position, as well as the start and end image of the polyp and a box for the non-expert annotators. Afterward, Endodata [9] is filtered and the relevant Paris and NICE classification parts are extracted to create the final data set used in this paper.…”

Section: Parismentioning

confidence: 99%

“…This image is the input image to our system, and the next step is the polyp detection system. For the polyp detection system, we used ENDOMIND-Adcanced [9], which is a polyp detection system. The system was developed by us using a post-processing technique based on video detection to work in real-time with a stream of images.…”

Section: Polyp Detection Systemmentioning

confidence: 99%

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Automated classification of polyps using deep learning architectures and few-shot learning

Krenzer

Heil

Fitting

et al. 2022

Preprint

Self Cite

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Background: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods: We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results: For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion: Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.

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Section: Discussionmentioning

confidence: 76%

“…Table 9 Test results of each model on two different test data sets, the SUN Colonoscopy Video data set and our own data set (EndoData) [9]. All values are given in %.…”

Section: Discussionmentioning

confidence: 99%

Section: Parismentioning

confidence: 99%

Section: Parismentioning

confidence: 99%

Section: Polyp Detection Systemmentioning

confidence: 99%

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Automated classification of polyps using deep learning architectures and few-shot learning

Krenzer

Heil

Fitting

et al. 2022

Preprint

Self Cite

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PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Saad,

Maghraby,

Badawy

2023

Neural Comput & Applic

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Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment.

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Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability

Houwen¹,

Nass²,

Vleugels³

et al. 2023

Gastrointestinal Endoscopy

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A Real-Time Polyp Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks

Cited by 5 publications

References 57 publications

Automated classification of polyps using deep learning architectures and few-shot learning

Automated classification of polyps using deep learning architectures and few-shot learning

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability

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