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AttSiOff: a self-attention-based approach on siRNA design with inhibition and off-target effect prediction

Med-X 2024年第1期2卷 59-69页

作者： Bin Liu Ye Yuan Xiaoyong Pan Hong-Bin Shen Cheng Jin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghaiChina Medical Robot Research Institute School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Small interfering RNA(siRNA)is often used for function study and expression regulation of specific genes,as well as the development of small molecule *** siRNAs with high inhibition and low off-target effects from massive can-didates is always a great *** experimentally-validated samples can prompt the development of machine-learning-based algorithms,including Support Vector Machine(SVM),Convolutional Neural Network(CNN),and Graph Neural Network(GNN).However,these methods still suffer from limited accuracy and poor generalization in designing potent and specific *** this study,we propose a novel approach for siRNA inhibition and off-target effect prediction,named *** combines a self-attention-based siRNA inhibition predictor with an mRNA searching package and an off-target *** predictor gives the inhibition score via analyzing the embedding of siRNA and local mRNA sequences,generated from the pre-trained RNA-FM model,as well as other meaningful prior-knowledge-based ***-attention mechanism can detect potentially decisive features,which may determine the inhibition of *** captures global and local dependencies more efficiently than normal *** tenfold cross-validation results indicate that our model outperforms all existing methods,achieving PCC of 0.81,SPCC of 0.84,and AUC of *** also reaches better performance of generalization and robustness on cross-dataset *** addition,the mRNA searching package could find all mature mRNAs for a given gene name from the GENOMES database,and the off-target filter can calculate the amount of unwanted off-target binding sites,which affects the specificity of *** on five mature siRNA drugs,as well as a new target gene(AGT),show that AttSioff has excellent convenience and operability in practical applications.

关键词： siRNA inhibition prediction Off-target effects Self-attention mechanism

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Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis 1

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1st International Conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Xu, Yunjiao Yang, Jie Guo, Tianjiao Scientific Research Office Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789819633494

ISBN: (纸本)9789819633487

The seven-field classification plays a key role in ophthalmology, especially in the diagnosis and treatment of diabetic retinopathy. In this paper, we designed a framework that can classify 7 fields of each eye simultaneously, and applied it to the medical-assisted interactive system of 7 fields of classification。F1 and F2 field of view are detected through a combination of algorithms based on decision rules and convolutional neural networks. The total accuracy on the internal test set is 98.7%(95%CI, 98.4%–99.1%). In external tests, the total accuracy of the algorithm in this paper on the LONGITUDINAL data set is 98.6%(95%CI,97.9%–99.3%). The total accuracy on the Drishti-GS data set is 100%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Anatomy recognition in CT images of head and neck region via precision atlases

Anatomy recognition in CT images of head and neck region via...

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Medical Imaging 2021: image processing

作者： Li, Jieyu Udupa, Jayaram K. Tong, Yubing Odhner, Dewey Torigian, Drew A. Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Medical Image Processing Group Department of Radiology University of Pennsylvania United States

ISBN: (纸本)9781510640214

Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in the segmentation procedure reduce its practicality in clinical application. In this work, we start from a viewpoint that different parts of the target object can be recognized by different atlases and propose a precision atlas selection strategy. By comparing regional similarity between target image and atlases, precision atlases are ranked and selected by the frequency of regional best match, which have no need to be globally similar to the target subject at either image-level or object-level, largely increasing the implicit patterns contained in the atlas set. In the proposed anatomy recognition method, atlas building is first achieved by all-totemplate registration, where the minimum spanning tree (MST) strategy is used to select a registration template from a subset of radiologically near-normal images. Then, a two-stage recognition process is conducted: in rough recognition, sub-image level similarity is calculated between the test image and each image of the whole atlas set, and only the atlas with the highest similarity contributes to the recognition map regionally;in refined recognition, the atlases with the highest frequencies of best match are selected as the precision atlases and are utilized to further increase the accuracy of boundary matching. The proposed method is demonstrated on 298 computed tomography (CT) images and 9 organs in the Head and Neck (HN) body region. Experimental results illustrate that our method is effective for organs with different segmentation challenge and samples with different image quality, where remarkable improvement in boundary interpretation is made by refined recognition and most objects achieve a localization error within 2 voxels. © 2021 SPIE.

关键词： image enhancement

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Fre-Yolo: Feature Refinement Extraction Network with Yolo for Blade Tip Small Point Light Detection

SSRN

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SSRN 2024年

作者： Zheng, Wenhao Xiong, Bangshu Yi, Hui Au, Qiaofeng Chen, Jiujiu Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China China Helicopter Research and Development Institute China

Detecting blade tip point light sources based on airborne computer vision is a critical step in measuring blade tip distance for coaxial unmanned helicopters. However, detecting blade tip point light sources quickly and highly accurately in a helicopter airborne environment is a challenge due to the extremely tiny imaging size of the blade tip point light sources. To this end, we propose a FRE-YOLO for detecting blade tip point light sources based on the YOLO network structure. In this work, we focus on designing a Feature Refinement Extraction network (FRE), which can greatly enrich the feature information of small targets during Backbone downsampling. Secondly, we also design a new paradigm Smooth Gaussian Distance (SmoothGD) which measures the degree of target bounding box matching. SmoothGD is mainly applied to regression loss calculation and label assigner, which can effectively improve the small target detection accuracy. Meanwhile, we construct the Blade Tip Point Light (BTPL) dataset for the network training. In addition, in order to evaluate the effectiveness of the proposed method more fairly, this paper conducts the same experiments on BTPL and the small target dataset VisDrone on MMYOLO. A large amount of experimental data shows that the proposed method has a positive impact on the detection of tiny point sources at the tip of the blade. It is also at the advanced level in the field of generalized small object detection. © 2024, The Authors. All rights reserved.

关键词： Extraction

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Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

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IEEE International Conference on Intelligent Computer Communication and processing (ICCP)

作者： Corvin-Petruţ Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap.

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image = Structure + Few Colors

Image = Structure + Few Colors

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Joint IAPR International Workshops on Structural, Syntactic and Statistical Techniques in pattern recognition, S+SSPR 2020

作者： Batavia, Darshan Gonzalez-Diaz, Rocio Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria Applied Math I University of Seville Seville Spain

ISBN: (纸本)9783030739720

Topology plays an important role in computer vision by capturing the structure of the objects. Nevertheless, its potential applications have not been sufficiently developed yet. In this paper, we combine the topological properties of an image with hierarchical approaches to build a topology preserving irregular image pyramid (TIIP). The TIIP algorithm uses combinatorial maps as data structure which implicitly capture the structure of the image in terms of the critical points. Thus, we can achieve a compact representation of an image, preserving the structure and topology of its critical points (maxima, the minima and the saddles). The parallel algorithmic complexity of building the pyramid is O(log d) where d is the diameter of the largest object. We achieve promising results for image reconstruction using only a few color values and the structure of the image, although preserving fine details including the texture of the image. © 2021, Springer Nature Switzerland AG.

关键词： image reconstruction

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Visual Odometry Drift Reduction Based on LiDAR Point Clouds Alignment 17

Visual Odometry Drift Reduction Based on LiDAR Point Clouds ...

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17th IEEE International Conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Golban, Cǎtǎlin-Cosmin Cobârzan, Corvin-Petruţ Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Department Romania Technical University of Cluj-Napoca Image Processing and Pattern Recognition Group Romania

ISBN: (纸本)9781665409766

Visual and LiDAR based odometry methods are key enablers for autonomous robots sub-problems such as mapping and localization or temporal aggregation and fusion of sensor data. When it comes to trajectory reconstruction the errors of independent local estimations accumulate over time, resulting in overall drifts from the real trajectory. This paper proposes a method for reducing the global drift by correcting the video-based frame-to-frame trajectory estimation at selected points in time based on the alignment of past and current LiDAR 3D measurements. The main strength of the proposed method is the formulation of the problem as a system of linear equations, which is an adequate numerical approximation as the corrective component is small enough. In this way we eliminate the need to deploy expensive non-linear optimization methods for calculating the correction transform. © 2021 IEEE.

关键词： Numerical methods

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research on the Strike Strategy of Quadcopter Unmanned Aerial Vehicles for Mobile Targets

Research on the Strike Strategy of Quadcopter Unmanned Aeria...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Wencai Zhang Junjie Sun Yanying Li Shen Yan Ning Zhang Jiaqi Wang Zhihua Chen Mingran Pan Engineering Technicians of Norinco Group Liao Shen Industries Group CO.LTD Shenyang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

This paper studies a strike path strategy for quadcopter drones targeting ground maneuvering targets. The strategy sets the strike path to two different strike speeds, which improves the stability and robustness of quadcopter drones while shortening strike time and increasing hit rates. Consider the attitude control of quadcopter unmanned aerial vehicles during motion, and verify the flight reliability of the mechanism through simulation experiments. Set different slope strike paths and obtain the optimal strike path through experiments, while proving the effectiveness of this strike strategy in engineering applications.

关键词： Attitude control Prototypes Autonomous aerial vehicles Stability analysis Robustness Robotics and automation Aircraft propulsion

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series Data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Distilling Object Detectors with Global Knowledge

arXiv

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arXiv 2022年

作者： Tang, Sanli Zhang, Zhongyu Cheng, Zhanzhan Lu, Jing Xu, Yunlu Niu, Yi He, Fan Hikvision Research Institute Hangzhou China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations, which is the instance-level knowledge only from the teacher model, i.e., the local knowledge. However, the empirical studies show that the local knowledge is much noisy in object detection tasks, especially on the blurred, occluded, or small instances. Thus, a more intrinsic approach is to measure the representations of instances w.r.t. a group of common basis vectors in the two feature spaces of the teacher and the student detectors, i.e., global knowledge. Then, the distilling algorithm can be applied as space alignment. To this end, a novel prototype generation module (PGM) is proposed to find the common basis vectors, dubbed prototypes, in the two feature spaces. Then, a robust distilling module (RDM) is applied to construct the global knowledge based on the prototypes and filtrate noisy local knowledge by measuring the discrepancy of the representations in two feature spaces. Experiments with Faster-RCNN and RetinaNet on PASCAL and COCO datasets show that our method achieves the best performance for distilling object detectors with various backbones, which even surpasses the performance of the teacher model. We also show that the existing methods can be easily combined with global knowledge and obtain further improvement. © 2022, CC BY-NC-ND.

关键词： Distillation

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