Gotowa bibliografia na temat „REAL IMAGE PREDICTION”

Average backfat thickness measurements (liveweight of 92.5 kg) were made on 276 pigs using the Krautkramer USK7 ultrasonic machine. Immediately preceding and 1 h after slaughter real-time ultrasonic images were made between the 3rd and 4th last ribs with the Tokyo Keiki LS-1000 (n = 149) and/or CS-3000 (n = 240) machines. Image analysis software was used to measure fat thickness (FT), muscle depth (MD) and area (MA) as well as scoring the number of objects, object area and percentage object area of the loin to be used for predicting meat quality. Carcasses were also graded by the Hennessy Grading Probe (HGP). Prediction equations for lean in the primal cuts based on USK7 and LS-1000 animal fat measurements had R2-values (residual standard deviations, RSD) of 0.62 (27.0) and 0.66 (25.7). Adding MD or MA to LS-1000 FT measurements increased the R2-values to 0.68 and 0.66. Prediction equations using animal fat measurements made by the USK7 and CS-3000 had R2-values (RSD) of 0.66 (26.5) and 0.76 (22.4). Adding MD or MA to CS-3000 FT measurements made no further improvement in the R2-values. Estimation of commercial lean yield from carcass FT and MD measurements made by the HGP and LS-1000 had R2-values (RSD) of 0.58 (1.72) and 0.65 (1.56). Adding MA to LS-1000 measurements made no further improvement in the R2-values. Prediction equations based on carcass FT and MD measurements made by the HGPandCS-3000 had R2-values (RSD) of 0.68 (1.65) and 0.72 (1.54). Adding MA to CS-3000 measurements made no further improvement in the prediction equation. It was concluded that RTU has most value for predicting carcass lean content and further improvements in precision will come from more accurate FT measurements from RTU images made by image analysis software. Correlation of the number of objects, object area and of percent object area of image from RTU images with intramuscular fat or marbling score made on the live pig or carcass were low, and presently do not appear suitable for predicting intramuscular fat. Key words: Carcass composition, meat quality, marbling, intramuscular fat, sex, pigs

Abstract 3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.