| Reference : Leveraging state-of-the-art architectures by enriching training information - a case study |
| Diverse speeches and writings : Speeches/Talks | |||
| Engineering, computing & technology : Multidisciplinary, general & others | |||
| Systems Biomedicine | |||
| http://hdl.handle.net/10993/48102 | |||
| Leveraging state-of-the-art architectures by enriching training information - a case study | |
| English | |
| Sölter, Jan [] | |
| Proverbio, Daniele [] | |
| Baniasadi, Mehri [] | |
| Bossa, Matias Nicolas [] | |
| Vlasov, Vanja [] | |
| Garcia Santa Cruz, Beatriz [] | |
Husch, Andreas  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >] | |
| 11-Jan-2021 | |
| International | |
| COVID 19-20 Lung CT Lesion Segmentation Grand Challenge Mini-symposium | |
| 2021-01-11 | |
| Children’s National Hospital | |
| Nvidia | |
| National Institutes of Health | |
| USA | |
| [en] 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. | |
| Researchers | |
| http://hdl.handle.net/10993/48102 | |
| FnR ; FNR14702831 > Andreas Husch > AICovIX > Ai Based Diagnosis Of Covid-19 From Ct/X-ray Imaging > 01/06/2020 > 30/11/2020 > 2020 |
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