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Other research product . Lecture . 2021

Leveraging state-of-the-art architectures by enriching training information - a case study

Sölter, Jan; Proverbio, Daniele; Baniasadi, Mehri; Bossa, Matias Nicolas; Vlasov, Vanja; Garcia Santa Cruz, Beatriz; Husch, Andreas;
Open Access
Published: 11 Jan 2021
Country: Luxembourg

Our working hypothesis is that key factors in COVID-19 imaging are the available imaging data and their label noise and confounders, rather than network architectures per se. Thus, we applied existing state-of-the-art convolution neural network frameworks based on the U-Net architecture, namely nnU-Net [3], and focused on leveraging the available training data. We did not apply any pre-training nor modi ed the network architecture. First, we enriched training information by generating two additional labels for lung and body area. Lung labels were created with a public available lung segmentation network and weak body labels were generated by thresholding. Subsequently, we trained three di erent multi-class networks: 2-label (original background and lesion labels), 3-label (additional lung label) and 4-label (additional lung and body label). The 3-label obtained the best single network performance in internal cross-validation (Dice-Score 0.756) and on the leaderboard (Dice- Score 0.755, Haussdor 95-Score 57.5). To improve robustness, we created a weighted ensemble of all three models, with calibrated weights to optimise the ranking in Dice-Score. This ensemble achieved a slight performance gain in internal cross-validation (Dice-Score 0.760). On the validation set leaderboard, it improved our Dice-Score to 0.768 and Haussdor 95- Score to 54.8. It ranked 3rd in phase I according to mean Dice-Score. Adding unlabelled data from the public TCIA dataset in a student-teacher manner signi cantly improved our internal validation score (Dice-Score of 0.770). However, we noticed partial overlap between our additional training data (although not human-labelled) and nal test data and therefore submitted the ensemble without additional data, to yield realistic assessments.


: Multidisciplinary, general & others [C99] [Engineering, computing & technology], : Multidisciplinaire, généralités & autres [C99] [Ingénierie, informatique & technologie]

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