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Computer vision has useful applications in precision medicine and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces’ visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem; however, the sparse label distribution, having few labeled samples, and huge class imbalances across categories make representation learning and generalization harder. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor. Our quantitative results on GestaltMatcher Database (GMDB) show that our CNN baseline surpasses previous works, including GestaltMatcher, and few-shot meta-learning strategies improve retrieval performance in frequent and rare classes.

Cancer is a major public health issue and takes the second-highest toll of deaths caused by non-communicable diseases worldwide. Automatically detecting lesions at an early stage is essential to increase the chance of a cure. This study proposes a novel dilated Faster R-CNN with modulated deformable convolution and modulated deformable positive-sensitive region of interest pooling to detect lesions in computer tomography images. A pre-trained VGG-16 is transferred as the backbone of Faster R-CNN, followed by a region proposal network and a region of interest pooling layer to achieve lesion detection. The modulated deformable convolutional layers are employed to learn deformable convolutional filters, while the modulated deformable positive-sensitive region of interest pooling provides an enhanced feature extraction on the feature maps. Moreover, dilated convolutions are combined with the modulated deformable convolutions to fine-tune the VGG-16 model with multi-scale receptive fields. In the experiments evaluated on the DeepLesion dataset, the modulated deformable positivesensitive region of interest pooling model achieves the highest sensitivity score of 58.8 % on average with dilation of [4,4,4] and outperforms state-of-the-art models in the range of [2,8] average false positives per image. This research demonstrates the suitability of dilation modifications and the possibility of enhancing the performance using a modulated deformable positive-sensitive region of interest pooling layer for universal lesion detectors.

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.

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