RESUMEN
LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the 'L-DIG' (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.
RESUMEN
AIM: Depressive disorder is often evaluated using established rating scales. However, consistent data collection with these scales requires trained professionals. In the present study, the "rater & estimation-system" reliability was assessed between consensus evaluation by trained psychiatrists and the estimation by 2 models of the AI-MADRS (Montgomery-Asberg Depression Rating Scale) estimation system, a machine learning algorithm-based model developed to assess the severity of depression. METHODS: During interviews with trained psychiatrists and the AI-MADRS estimation system, patients responded orally to machine-generated voice prompts from the AI-MADRS structured interview questions. The severity scores estimated from two models of the AI-MADRS estimation system, the max estimation model and the average estimation model, were compared with those by trained psychiatrists. RESULTS: A total of 51 evaluation interviews conducted on 30 patients were analyzed. Pearson's correlation coefficient with the scores evaluated by trained psychiatrists was 0.76 (95% confidence interval 0.62-0.86) for the max estimation model, and 0.86 (0.76-0.92) for the average estimation model. The ANOVA ICC rater & estimation-system reliability with the evaluation scores by trained psychiatrists was 0.51 (-0.09 to 0.79) for the max estimation model, and 0.75 (0.55-0.86) for the average estimation model. CONCLUSION: The average estimation model of AI-MADRS demonstrated substantially acceptable rater & estimation-system reliability with trained psychiatrists. Accumulating a broader training dataset and the refinement of AI-MADRS interviews are expected to improve the performance of AI-MADRS. Our findings suggest that AI technologies can significantly modernize and potentially revolutionize the realm of depression assessments.