Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes

BackgroundSoft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.ObjectivesThe study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.MethodsWe retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.ResultsThe AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01).ConclusionThe AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance

Enhanced Hybrid Ant Colony Optimization for Machining Line Balancing Problem with Compound and Complex Constraints

Targeted at the machining production line balancing problem, based on the precedence constraint relation of the present machining task, this article suggests adding practical constraints such as advanced station preparations, post-auxiliary tasks, and tool changing. The study introduced ‘tight’ and ’or’ constraints to bring the problem definition closer to the actual situation. For this problem, a mixed-integer programming model was constructed in this study. The model redefines the machining and auxiliary processing tasks and adds new time constraints to the station. The model considers two optimisation objectives: the number of stations and the machining line balancing rate. In view of the complexity of the problem, heuristic task set filtering mechanisms were designed and added to the ant colony optimisation, to satisfy the above compound and complex constraints. The processing task chain was constructed using the rules of ant colony pheromone accumulation and a random search mechanism. The study designed a Gantt chart generation module to improve the usability and visibility of the program. Ultimately, through an actual case study of a complex box part including 73 processing elements and realising the design and planning of machining production lines that meet complex constraints by substituting algorithms, the balance rates of several groups of optimisation schemes were higher than 90%, which showed that the algorithm is effective and has a good economy and practicability

End to end multitask joint learning model for osteoperosis classification in CT images

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present