Sofus Rischel's scientific contributions

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Publications (2)

Figure 1. a) Normal chest X-ray showing the lungs clearly b) Abnormal...
Figure 3. Chest X-rays with and without augmentation. (a) No...
Figure 4. Box plot of test set performance for the baseline and...
Figure 5. a) Three test set samples with highest and least dice...
Figure 8. Block masking augmentation
Lung Segmentation from Chest X-rays using Variational Data Imputation
Conference Paper
Full-text available
  • Jul 2020
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible , making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from...
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Figure 1. a) Normal chest X-ray showing the lungs clearly b) Abnormal...
Figure 3. Chest X-rays with and without augmentation. (a) No...
Figure 4. Box plot of test set performance for the baseline and...
Figure 5. a) Three test set samples with highest and least dice...
+1 Figure 6. Input images a) before and b) after histogram equalization
Lung Segmentation from Chest X-rays using Variational Data Imputation
Preprint
Full-text available
  • May 2020
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from s...
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Download full-text

Citations

... In our implementation, lung segmentation has been carried out using the solution proposed by Selvan et al. [37], based on an U-net architecture and a variational encoder for data imputation, trained on public datasets labeled for tuberculosis detection [24], and specifically the model provided by the authors 1 . The segmentation masks are post-processed using connected component analysis to exclude small erroneous regions and binary morphological closing to fill any small holes in the masks (see Figure 3 for examples). ...
Reference: Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification
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