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Your search keyword '"Sun, Ruitong"' showing total 13 results
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13 results on '"Sun, Ruitong"'

1. Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

Author

Sun, Ruitong and Rostami, Mohammad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Manual annotation of 3D medical images for segmentation tasks is tedious and time-consuming. Moreover, data privacy limits the applicability of crowd sourcing to perform data annotation in medical domains. As a result, training deep neural networks for medical image segmentation can be challenging. We introduce a new source-free Unsupervised Domain Adaptation (UDA) method to address this problem. Our idea is based on estimating the internally learned distribution of a relevant source domain by a base model and then generating pseudo-labels that are used for enhancing the model refinement through self-training. We demonstrate that our approach leads to SOTA performance on a real-world 3D medical dataset.

Published
2024

2. Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning

Author

Sun, Ruitong and Rostami, Mohammad

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Melanoma is a prevalent lethal type of cancer that is treatable if diagnosed at early stages of development. Skin lesions are a typical indicator for diagnosing melanoma but they often led to delayed diagnosis due to high similarities of cancerous and benign lesions at early stages of melanoma. Deep learning (DL) can be used as a solution to classify skin lesion pictures with a high accuracy, but clinical adoption of deep learning faces a significant challenge. The reason is that the decision processes of deep learning models are often uninterpretable which makes them black boxes that are challenging to trust. We develop an explainable deep learning architecture for melanoma diagnosis which generates clinically interpretable visual explanations for its decisions. Our experiments demonstrate that our proposed architectures matches clinical explanations significantly better than existing architectures.

Published
2023

5. Evaluation of quercetin as a potential cytoprotector against acetaldehyde using the cultured hepatocyte model with aldehyde dehydrogenase isozyme deficiency.

Author

Xu, Yuhang, Sawamoto, Takeshi, Sun, Ruitong, Ishikura, Aki, Munemasa, Shintaro, Murata, Yoshiyuki, Satoh, Ayano, Matsumoto, Akiko, Nakamura, Toshiyuki, and Nakamura, Yoshimasa

Subjects
ALDEHYDE dehydrogenase, CYTOPROTECTION, QUERCETIN
Abstract

Protective effect of quercetin against acetaldehyde was evaluated using the cultured hepatocyte models with aldehyde dehydrogenase (ALDH) isozyme deficiency (aldh2-kd and aldh1a1-kd). The quercetin-induced cytoprotection against acetaldehyde in the ALDH1A1-deficient mutant (aldh1a1-kd) was weaker than that in the wild type. Furthermore, quercetin did not enhance the ALDH activity in aldh1a1-kd cells, suggesting that ALDH1A1 is involved in quercetin-induced cytoprotection. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm.

Author

Yuan, Qingni, Sun, Ruitong, and Du, Xiaoying

Subjects
POTENTIAL field method (Robotics), PARTICLE swarm optimization, ROBOTIC path planning, MATHEMATICAL optimization, DIFFERENTIAL evolution, SEARCH algorithms
Abstract

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a "high-intensity training" mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem. [ABSTRACT FROM AUTHOR]

Published
2023
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