Publications by authors named "Chenglang Lu"

Automatic segmentation of breast tumor ultrasound images can provide doctors with objective and efficient references for lesions and regions of interest. Both dataset optimization and model structure optimization are crucial for achieving optimal image segmentation performance, and it can be challenging to satisfy the clinical needs solely through model structure enhancements in the context of insufficient breast tumor ultrasound datasets for model training. While significant research has focused on enhancing the architecture of deep learning models to improve tumor segmentation performance, there is a relative paucity of work dedicated to dataset augmentation.

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The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO.

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From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement.

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In medical information systems, there are a lot of confidential information about patient privacy. It is therefore an important problem how to prevent patient's personal privacy information from being disclosed. Although traditional security protection strategies (such as identity authentication and authorization access control) can well ensure data integrity, they cannot prevent system's internal staff (such as administrators) from accessing and disclosing patient privacy information.

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Percutaneous renal biopsy is frequently performed, and postbiopsy arteriovenous fistula (AVF) formation is not uncommon. In most cases, it is asymptomatic and remains undiagnosed, because Doppler examination is not performed routinely. We describe 2 cases of postbiopsy AVF of the kidney that were detected 61 and 17 months after renal biopsy, respectively.

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