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. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice. Aim: The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them. Methods: This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month. Discussion: This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field. Clinical Trial Registration: Identifier: UMIN000032141.

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. By replacing traditional stacked RGB frames with residual ones, 35.6% and 26.6% points improvements over top-1 accuracy can be achieved on the UCF101 and HMDB51 datasets when trained from scratch using ResNet-18-3D. We deeply analyze the effectiveness of this modality compared to normal RGB video clips, and find that better motion features can be extracted using residual frames with 3D ConvNets. Considering that residual frames contain little information of object appearance, we further use a 2D convolutional network to extract appearance features and combine them together to form a two-path solution. In this way, we can achieve better performance than some methods which even used an additional optical flow stream. Moreover, the proposed residual-input path can outperform RGB counterpart on unseen datasets when we apply trained models to video retrieval tasks. Huge improvements can also be obtained when the residual inputs are applied to video-based self-supervised learning methods, revealing better motion representation and generalization ability of our proposal.

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. This study proposes a deep neural network architecture, called DeepSperm, that solves the aforementioned problems and is more accurate and faster than state-of-the-art architectures. METHODS: In the proposed architecture, we use only one detection layer, which is specific for small object detection. For handling overfitting and increasing accuracy, we set a higher input network resolution, use a dropout layer, and perform data augmentation on saturation and exposure. Several hyper-parameters are tuned to achieve better performance. Mean average precision (mAP), confusion matrix, precision, recall, and F1-score are used to measure accuracy. Frame per second (fps) is used to measure speed. We compare our proposed method with you only look once (YOLO) v3 and YOLOv4. RESULTS: In our experiment, we achieve 94.11 mAP on the test dataset, F1-score of 0.93, and a processing speed of 51.9 fps. In comparison with YOLOv4, our proposed method is 2.18 x faster on testing, and 2.9 x faster on training with a small dataset, while achieving comparative detection accuracy. The weights file size was also reduced significantly, with one-twentieth that of YOLOv4. Moreover, it requires a 1.07 x less graphical processing unit (GPU) memory than YOLOv4. CONCLUSIONS: This study proposes DeepSperm, which is a simple, effective, and efficient deep neural network architecture with its hyper-parameters and configuration to detect bull sperm cells robustly in real time. In our experiments, we surpass the state-of-the-art in terms of accuracy, speed, and resource needs.

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. In other cases, experienced observers are required to extract the feature values or to provide some interactions with the system. Even if experts are involved, interobserver variance becomes another problem. In addition, most of the works use only one or a few slice(s) because 3D tumor segmentation is time consuming. In this paper, we propose a tumor classification system that analyzes the luminance distribution of the whole tumor region. Typical cases are classified by the luminance range thresholding and the apparent diffusion coefficients (ADC) thresholding. Nontypical cases are classified by a support vector machine (SVM). Most of the processing elements are semiautomatic. Therefore, even novice users can use the system easily and get the same results as experts. The experiments were conducted using 40 MRI datasets. The classification accuracy of the proposed method was 91.1% without the ADC thresholding and 95.4% with the ADC thresholding. On the other hand, the baseline method, the conventional ADC thresholding, yielded only 67.5% accuracy.