Efficient-Lightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images.

The most significant technical challenges of current aerial image object-detection tasks are the extremely low accuracy for detecting small objects that are densely distributed within a scene and the lack of semantic information. Moreover, existing detectors with large parameter scales are unsuitable for aerial image object-detection scenarios oriented toward low-end GPUs. To address this technical challenge, we propose efficient-lightweight You Only Look Once (EL-YOLO), an innovative model that overcomes the limitations of existing detectors and low-end GPU orientation. EL-YOLO surpasses the baseline models in three key areas. Firstly, we design and scrutinize three model architectures to intensify the model's focus on small objects and identify the most effective network structure. Secondly, we design efficient spatial pyramid pooling (ESPP) to augment the representation of small-object features in aerial images. Lastly, we introduce the alpha-complete intersection over union (α-CIoU) loss function to tackle the imbalance between positive and negative samples in aerial images. Our proposed EL-YOLO method demonstrates a strong generalization and robustness for the small-object detection problem in aerial images. The experimental results show that, with the model parameters maintained below 10 M while the input image size was unified at 640 × 640 pixels, the APS of the EL-YOLOv5 reached 10.8% and 10.7% and enhanced the APs by 1.9% and 2.2% compared to YOLOv5 on two challenging aerial image datasets, DIOR and VisDrone, respectively.

Medical expert and machine learning analysis of lumbar disc herniation based on magnetic resonance imaging.

OBJECTIVE: Observation and statistical analysis was used to evaluate the ability of lumbar disc magnetic resonance imaging (MRI) to obtain the smallest size of Al2O3 spots (calcified foci) and lumbar disc fiber signals. METHODS: First, we perform image acquisition of the MRI, perform the statistical analysis using five different sizes of Al2O3 spots and lumbar disc fibers on the imaging plate (IP), use a molybdenum target MRI machine 26 kV, adjust the milliampere amount, select the appropriate image processing parameters, and obtain the experimental image of the density value (D=0.70±0.05), the 5-point judgment method is used to obtain the total score of 10 lines of signals composed of 5 signals and noise, and a group is computed using the statistical analysis that is built from human observation and machine prediction (based on machine learning), which are then compared. In particular, we implemented a convolutional neural network algorithm to evaluate the medical condition against human observers, so as to study the structure of the lumbar intervertebral disc. We compute the true positive probability P(S/s) and false positive probability P(S/n) values, draw ROC curve, and compute the judgment probability value of each signal Pdet. We then use SPSS 10.0 statistical single factor analysis of variance software to process the data, and obtain the smallest calcified focus and lumbar disc mass focus. RESULTS: Using probability statistical methods to obtain the data of the ROC curve and the average value of the judgment probability Pdet, among 5 different sizes Al2O3 spots (calcifications), 0.20mm Pdet= 0.6250minimum, 0.55mm Pdet = 0.9000 the largest, but the difference between 0.20mm and 0.25mm Pdet is not statistically significant, and the difference is statistically significant; among the five types of lumbar disc fibers (tumor foci) of different sizes, 0.45mm Pdet= 0.5313minimum, 1.00mm Pdet =0.8813 is the largest, while the difference between 0.45mm and 0.60mm is not statistically significant, and the difference between 0.45mm and other is statistically significant. We note that the human observation and machine learning prediction is not significantly different (P<0.05). CONCLUSIONS: The computation of the ROC curve and that of the probability of judgment using the statistical analysis based on a deep learning platform is simple and fast, and approximates that of human observation. It is suitable for the evaluation of image quality control carried out by daily clinical work.

Prognostic Significance of Preoperative Inflammatory Biomarkers and Traditional Clinical Parameters in Patients with Spinal Metastasis from Clear Cell Renal Cell Carcinoma: A Retrospective Study of 95 Patients in a Single Center.

PURPOSE: The purpose of this retrospective study was to identify preoperative inflammatory biomarkers and clinical parameters and evaluate their prognostic significance in patients with spinal metastasis from clear cell renal cell carcinoma (CCRCC). PATIENTS AND METHODS: Correlations of overall survival (OS) with traditional clinical parameters and inflammatory indicators including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), albumin-globulin ratio (AGR), and C-reactive protein to albumin ratio (CRP/Alb ratio) were analyzed in 95 patients with spinal metastasis from CCRCA using the Kaplan-Meier method to identify potential prognostic factors. Factors with P values ≤ 0.1 were subjected to multivariate analysis by Cox regression analysis. P values ≤ 0.05 were considered statistically significant. RESULTS: The 95 patients included in this study were followed up by a mean of 48.8 months (median 51 months; range 6-132 months), during which 21 patients died, with a death rate of 22.1%. The statistical results indicated that patients with total piecemeal spondylectomy (TPS), targeted therapy, NLR < 3.8 and PLR < 206.9 had a significantly longer OS rate. CONCLUSION: TPS and targeted therapy could significantly prolong the OS of patients with spinal metastasis from CCRCC. In addition, NLR and PLR are robust and convenient prognostic indicators that have a discriminatory ability superior to other inflammatory biomarkers.