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We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.Data physicalizations "map data to physical form," yet many canonical examples are not based on external data sets. To address this contradiction, I argue that the practice of physicalization forces us to rethink traditional notions of data. This article proposes a conceptual framework to examine how physicalizations relate to data. This article develops a two-dimensional conceptual space for comparing different perspectives on data used in physicalization, drawing from design theory and critical data studies literature. One axis distinguishes between epistemological and ontological perspectives, focusing on the relationship between data and the mind. The second axis distinguishes how data relate to the world, differentiating between representational and relational perspectives. To clarify the aesthetic and conceptual implications of these different perspectives, the article discusses examples of data physicalization for each quadrant of the continuous space. It further uses the framework to examine the explicit and implicit assumptions about data in physicalization literature. As a theoretical article, it encourages practitioners to think about how data relate to the manifestations and the phenomena they try to capture. It invites exploration of the relationship between data and the world as a generative source of creative tension.Image cropping aims to improve the composition and aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU.Many role-playing games feature character creation systems where players are allowed to edit the facial appearance of their in-game characters. This paper proposes a novel method to automatically create game characters based on a single face photo. selleck inhibitor We frame this "artistic creation" process under a self-supervised learning paradigm by leveraging the differentiable neural rendering. Considering the rendering process of a typical game engine is not differentiable, an "imitator" network is introduced to imitate the behavior of the engine so that the in-game characters can be smoothly optimized by gradient descent in an end-to-end fashion. Different from previous monocular 3D face reconstruction which focuses on generating 3D mesh-grid and ignores user interaction, our method produces fine-grained facial parameters with a clear physical significance where users can optionally fine-tune their auto-created characters by manually adjusting those parameters. Experiments on multiple large-scale face datasets show that our method can generate highly robust and vivid game characters. Our method has been applied to two games and has now provided over 10 million times of online services.

In this work the potential of non-invasive detection of knee osteoarthritis is investigated using the sounds generated by the knee joint during walking.

The information contained in the time-frequency domain of these signals and its compressed representations is exploited and their discriminant properties are studied. Their efficacy for the task of normal vs abnormal signal classification is evaluated using a comprehensive experimental framework. Based on this, the impact of the feature extraction parameters on the classification performance is investigated using Classification and Regression Trees, Linear Discriminant Analysis and Support Vector Machine classifiers.

It is shown that classification is successful with an area under the Receiver Operating Characteristic curve of 0.92.

The analysis indicates improvements in classification performance when using non-uniform frequency scaling and identifies specific frequency bands that contain discriminative features.

Contrary to other studies that focus on sit-to-stand movements and knee flexion/extension, this study used knee sounds obtained during walking. The analysis of such signals leads to non-invasive detection of knee osteoarthritis with high accuracy and could potentially extend the range of available tools for the assessment of the disease as a more practical and cost effective method without requiring clinical setups.

Contrary to other studies that focus on sit-to-stand movements and knee flexion/extension, this study used knee sounds obtained during walking. The analysis of such signals leads to non-invasive detection of knee osteoarthritis with high accuracy and could potentially extend the range of available tools for the assessment of the disease as a more practical and cost effective method without requiring clinical setups.