A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

Multi-modal Machine Learning in Engineering Design: A Review and Future Directions

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed

Frameless stereotactic biopsy for precision neurosurgery : diagnostic value, safety, and accuracy

BACKGROUND: Stereotactic biopsy is consistently employed to characterize cerebral lesions in patients who are not suitable for microsurgical resection. In the past years, technical improvement and neuroimaging advancements contributed to increase the diagnostic yield, the safety, and the application of this procedure. Currently, in addition to histological diagnosis, the molecular analysis is considered essential in the diagnostic process to properly select therapeutic and prognostic algorithms in a personalized approach. The present study reports our experience with frameless stereotactic brain biopsy in this molecular era. METHODS: One hundred forty consecutive patients treated from January 2013 to September 2018 were analyzed. Biopsies were performed using the Brainlab Varioguide\uae frameless stereotactic system. Patients' clinical and demographic data, the time of occupation of the operating room, the surgical time, the morbidity, and the diagnostic yield in providing a histological and molecular diagnosis were recorded and evaluated. RESULTS: The overall diagnostic yield was 93.6% with nine procedures resulting non-diagnostic. Among 110 patients with glioma, the IDH-1 mutational status was characterized in 108 cases (98.2%), resulting wild-type in all subjects but 3; MGMT methylation was characterized in 96 cases (87.3%), resulting present in 60 patients, and 1p/19q codeletion was founded in 6 of the 20 cases of grade II-III gliomas analyzed. All the specimens were apt for molecular analysis when performed. Bleeding requiring surgical drainage occurred in 2.1% of the cases; 8 (5.7%) asymptomatic hemorrhages requiring no treatment were observed. No biopsy-related mortality was recorded. Median length of hospital stay was 5 days (IQR 4-8) with mean surgical time of 60.77 min (\ub1\u200923.12) and 137.44\u2009\ub1\u200924.1 min of total occupation time of the operative room. CONCLUSIONS: Stereotactic frameless biopsy is a safe, feasible, and fast procedure to obtain a histological and molecular diagnosis