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Der Einsatz starrer Endoskope zur Entfernung von Fremdkörpern der Luft- und oberen Speisewege ist Thema dieser retrospektiven Studie, die sich ferner mit endoskopischen Alternativverfahren auseinandersetzt. Da bei einer Vielzahl von Patienten, die wegen Fremdkörperverdachts ösophageal und/oder tracheobronchial endoskopiert wurden, keine Fremdkörperpersistenz nachgewiesen werden konnten, soll die Frage nach den für dieses Phänomen verantwortlichen Faktoren geklärt werden. Die Anamnese, klinischer Befund und die Gegenüberstellung von Verdachtsdiagnose und endgültiger (postoperativer) Diagnose soll ätiologische Zusammenhänge klären und zeigt konkrete Anwendungsmöglichkeiten der gewonnenen Erkenntnisse auf.
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.