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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Hybrid Loss with Network Trimming for Disease Recognition in Gastrointestinal Endoscopy
verfasst von : Qi He, Sophia Bano, Danail Stoyanov, Siyang Zuo
Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges
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Abstract
EndoTect Challenge 2020, which aims at the detection of gastrointestinal diseases and abnormalities, consists of three tasks including Detection, Efficient Detection and Segmentation in endoscopic images. Although pathologies belonging to different classes can be manually separated by experienced experts, however, existing classification models struggle to discriminate them due to low inter-class variability. As a result, the models’ convergence deteriorates. To this end, we propose a hybrid loss function to stabilise model training. For the detection and efficient detection tasks, we utilise ResNet-152 and MobileNetV3 architectures, respectively, along with the hybrid loss function. For the segmentation task, Cascade Mask R-CNN is investigated. In this paper, we report the architecture of our detection and segmentation models and the performance of our methods on HyperKvasir and EndoTect test dataset.