Digitizing translucent object appearance by validating computed optical properties.

The optical properties available for an object are most often fragmented and insufficient for photorealistic rendering of the object. We propose a procedure for digitizing a translucent object with sufficient information for predictive rendering of its appearance. Based on object material descriptions, we compute optical properties and validate or adjust this object appearance model based on comparison of simulation with spectrophotometric measurements of the bidirectional scattering-surface reflectance distribution function (BSSRDF). To ease this type of comparison, we provide an efficient simulation tool that computes the BSSRDF for a particular light-view configuration. Even with just a few configurations, the localized lighting in BSSRDF measurements is useful for assessing the appropriateness of computed or otherwise acquired optical properties. To validate an object appearance model in a more common lighting environment, we render the appearance of the obtained digital twin and assess the photorealism of our renderings through pixel-by-pixel comparison with photographs of the physical object.

Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty.

Objectives: In this study, we propose a diagnostic model for automatic detection of otitis media based on combined input of otoscopy images and wideband tympanometry measurements. Methods: We present a neural network-based model for the joint prediction of otitis media and diagnostic difficulty. We use the subclassifications acute otitis media and otitis media with effusion. The proposed approach is based on deep metric learning, and we compare this with the performance of a standard multi-task network. Results: The proposed deep metric approach shows good performance on both tasks, and we show that the multi-modal input increases the performance for both classification and difficulty estimation compared to the models trained on the modalities separately. An accuracy of 86.5% is achieved for the classification task, and a Kendall rank correlation coefficient of 0.45 is achieved for difficulty estimation, corresponding to a correct ranking of 72.6% of the cases. Conclusion: This study demonstrates the strengths of a multi-modal diagnostic tool using both otoscopy images and wideband tympanometry measurements for the diagnosis of otitis media. Furthermore, we show that deep metric learning improves the performance of the models.