Search Results (2)

This essay discusses teaching screenwriting in terms of translation and adaptation. Realigning terminology with everyday language, translation is redefined as an invariance-based phenomenon while adaptation is reconceived as a variance-based phenomenon, which entails better fit. More specific working definitions follow specifying what one could be teaching or learning in more precise terms. The acceptance of these proposals remains a matter of contention. One major obstacle involves the current Western Romantic view on art and culture. Having driven a rift between art and craft, Romanticism 2.0 opposes the aforesaid working definitions and disparages screenwriting, translation, and adaptation, lest they comply with the Romantic rule. Suggestions follow to re-open the Romantic view to its pre-Romantic stance and to revalue both art and craft values in screenwriting, translation, and adaptation. Finally, conclusions highlight some caveats foreshadowing resistance also against nudging back Romanticism 2.0 to its pre-Romantic views. Full article

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models’ accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings. Full article