Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

Introduction: Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia.

Rethinking Motion Representation: Residual Frames With 3D ConvNets.

Recently, 3D convolutional networks yield good performance in action recognition. However, an optical flow stream is still needed for motion representation to ensure better performance, whose cost is very high. In this paper, we propose a cheap but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.

DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos.

Background And Objective: Object detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges that are more difficult than those presented by other general object-detection cases. These challenges include partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, low video resolution, and blurry objects because of the rapid movement of the sperm cells.

Classification of cerebral lymphomas and glioblastomas featuring luminance distribution analysis.

Differentiating lymphomas and glioblastomas is important for proper treatment planning. A number of works have been proposed but there are still some problems. For example, many works depend on thresholding a single feature value, which is susceptible to noise.