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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detection 39

SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liufu, Xing Tan, Chaolei Lin, Xiaotong Qi, Yonggang Li, Jinxuan Hu, Jian-Fang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China

ISBN: (纸本)157735897X

Edge labels are typically at various granularity levels owing to the varying preferences of annotators, thus handling the subjectivity of per-pixel labels has been a focal point for edge detection. Previous methods often employ a simple voting strategy to diminish such label uncertainty or impose a strong assumption of labels with a pre-defined distribution, e.g., Gaussian. In this work, we unveil that the segment anything model (SAM) provides strong prior knowledge to model the uncertainty in edge labels. Our key insight is that the intermediate SAM features inherently correspond to object edges at various granularities, which reflects different edge options due to uncertainty. Therefore, we attempt to align uncertainty with granularity by regressing intermediate SAM features from different layers to object edges at multi-granularity levels. In doing so, the model can fully and explicitly explore diverse "uncertainties" in a data-driven fashion. Specifically, we inject a lightweight module (∼ 1.5% additional parameters) into the frozen SAM to progressively fuse and adapt its intermediate features to estimate edges from coarse to fine. It is crucial to normalize the granularity level of human edge labels to match their innate uncertainty. For this, we simply perform linear blending to the real edge labels at hand to create pseudo labels with varying granularities. Consequently, our uncertainty-aligned edge detector can flexibly produce edges at any desired granularity (including an optimal one). Thanks to SAM, our model uniquely demonstrates strong generalizability for cross-dataset edge detection. Extensive experimental results on BSDS500, Muticue and NYUDv2 validate our model’s superiority. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

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LSketch: A Label-Enabled Graph Stream Sketch Toward Time-Sensitive Queries

arXiv

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arXiv 2023年

作者： Zeng, Yiling Song, Chunyao Li, Yuhan Ge, Tingjian Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin300350 China Department of Computer Science University of Massachusetts Lowell United States

Graph streams represent data interactions in real applications. The mining of graph streams plays an important role in network security, social network analysis, and traffic control, among others. However, the sheer volume and high dynamics cause great challenges for efficient storage and subsequent query analysis on them. Current studies apply sketches to summarize graph streams. We propose LSketch that works for heterogeneous graph streams, which effectively preserves the label information carried by the streams in real scenes, thereby enriching the expressive ability of sketches. In addition, as graph streams continue to evolve over time, edges too old may lose their practical significance. Therefore, we introduce the sliding window model into LSketch to eliminate the expired edges automatically. LSketch uses sub-linear storage space and can support structure based queries and time-sensitive queries with high accuracy. We perform extensive experiments over four real datasets, demonstrating the superiority of the proposed method over state-of-the-art methods, in aspects of query accuracy and time efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Network security

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Impossible Differential Analysis on 8-Round PRINCE 9th

Impossible Differential Analysis on 8-Round PRINCE

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9th International Conference on computer Engineering and Networks, CENet2019

作者： Ding, Yaoling Jia, Keting Wang, An Shi, Ying School of Computer Science Beijing Institute of Technology Beijing100081 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9789811537523

PRINCE is a lightweight block cipher, which was proposed by Borghoff et al. in Asiacrypt 2012. Various cryptanalytic techniques have been employed to evaluate the security of PRINCE. In 2017, Ding et al. constructed a 4-round impossible differential based on some observations on M′ operation and launched impossible differential attacks on 6- and 7-round PRINCE and the underlying PRINCEcore. In this paper, we explore the differential distribution table (DDT) of the S-box employed in PRINCE and construct a more detailed DDT which contains the input/output values corresponding to each differential. Taking advantage of the table, we compute the subkeys instead of guessing them. With this technique, we extend the impossible differential attacks of PRINCE and PRINCEcore to eight rounds. The corresponding computational and complexities are 110.7 and 62.26 encryptions, respectively, which are much less than exhaustive search. And the data complexities are 64 and 60 chosen plaintexts, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Intelligent Energy-Efficient and Fair Resource Scheduling for UAV-Assisted Space-Air-Ground Integrated Networks Under Jamming Attacks

Intelligent Energy-Efficient and Fair Resource Scheduling fo...

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IEEE Conference on Vehicular technology (VTC)

作者： Shihao Chen Helin Yang Liang Xiao Changyuan Xu Xianzhong Xie Wanting Yang Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China Chongqing Key Laboratory of Computer Network and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.

关键词： Wireless communication Vehicular and wireless technologies Simulation Autonomous aerial vehicles Space-air-ground integrated networks Energy efficiency Trajectory

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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition

arXiv

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arXiv 2024年

作者： Wang, Jingyao Qiang, Wenwen Zheng, Changwen Sun, Fuchun University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Fine-grained emotion recognition (FER) plays a vital role in various fields, such as disease diagnosis, personalized recommendations, and multimedia mining. However, existing FER methods face three key challenges in real-world applications: (i) they rely on large amounts of continuously annotated data to ensure accuracy since emotions are complex and ambiguous in reality, which is costly and time-consuming;(ii) they cannot capture the temporal heterogeneity caused by changing emotion patterns, because they usually assume that the temporal correlation within sampling periods is the same;(iii) they do not consider the spatial heterogeneity of different FER scenarios, that is, the distribution of emotion information in different data may have bias or interference. To address these challenges, we propose a Spatio-Temporal Fuzzy-oriented Multi-modal Meta-learning framework (ST-F2M). Specifically, ST-F2M first divides the multimodal videos into multiple views, and each view corresponds to one modality of one emotion. Multiple randomly selected views for the same emotion form a meta-training task. Next, ST-F2M uses an integrated module with spatial and temporal convolutions to encode the data of each task, reflecting the spatial and temporal heterogeneity. Then it adds fuzzy semantic information to each task based on generalized fuzzy rules, which helps handle the complexity and ambiguity of emotions. Finally, ST-F2M learns emotion-related general meta-knowledge through meta-recurrent neural networks to achieve fast and robust fine-grained emotion recognition. Extensive experiments show that ST-F2M outperforms various state-of-the-art methods in terms of accuracy and model efficiency. In addition, we construct ablation studies and further analysis to explore why ST-F2M performs well. Copyright © 2024, The Authors. All rights reserved.

关键词： Recurrent neural networks

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HoLens: A Visual Analytics Design for Higher-order Movement Modeling and Visualization

arXiv

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arXiv 2024年

作者： Feng, Zezheng Zhu, Fang Wang, Hongjun Hao, Jianing Yang, Shuang-Hua Zeng, Wei Qu, Huamin Hong Kong University of Science and Technology Hong Kong Southern University of Science and Technology China Department of Computer Science University of Reading United Kingdom Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and Technology China

Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analysis, which depicts only first-order geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements, then extract higher-order patterns from the DAG. However, DAG-based methods heavily rely on the identification of movement keypoints that are challenging for sparse movements and fail to consider the temporal variants that are critical for movements in urban environments. To overcome the limitations, we propose HoLens, a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment. HoLens mainly makes twofold contributions: first, we design an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity, contextual information, and temporal variability;second, we develop an interactive visual analytics interface consisting of well-established visualization techniques, including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions. Two real-world case studies manifest that the method can adaptively aggregate the data and exhibit the process of how to explore the higher-order patterns by HoLens. We also demonstrate our approach's feasibility, usability, and effectiveness through an expert interview with three domain experts. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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Ppdf-Fedtmi: A Federated Learning-Based Transport Mode Inference Model with Privacy-Preserving Data Fusion

SSRN

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SSRN 2023年

作者： Huang, Qihan Zhang, Jing Zeng, Zuanyang He, Ding Ye, Xiucai Chen, Yi School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer and Information Security Management Fujian Police College Fuzhou China

Mobile Crowd-Sensing (MCS) is a distributed data fusion system that leverages multiple data sources to improve the performance. By incorporating data from numerous individuals, MCS can provide more accurate, comprehensive, and valuable information than any single source could offer alone. As an essential application in MCS, intelligent transportation system deploys a large number of Transport Mode Inference (TMI) models. However, tens of thousands of trajectories are utilized for TMI model training, which will bring the risk of privacy disclosure. Existing solutions cannot address the privacy concerns of trajectories in MCS. In order to achieve the secure distributed architecture for eliminating such concerns in collaboration, a Federated Learning-based Transport Mode Inference Model with Privacy-Preserving Data Fusion (PPDF-FedTMI) is proposed in this paper. Firstly, federated learning is utilized in the TMI model to solve the isolated data islands problem of MCS systems. Subsequently, a fuzzy fusion algorithm based on the fuzzy clustering of label information is proposed to train models stably and effectively under non-IID distributed datasets. Furthermore, a privacy-preserving joint training method applying local differential privacy is designed to enhance the gradient privacy, which can prevent attackers from deriving the original through the gradient. Finally, the experimental results demonstrate that PPDF-FedTMI can provide higher accuracy of mode inference and effective privacy-preserving compared to other models. © 2023, The Authors. All rights reserved.

关键词： Data fusion

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Optimization Method for Cross-Domain Heterogeneous Storage system Management Based on Improved NSGA-III

Optimization Method for Cross-Domain Heterogeneous Storage S...

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International Conference on computer Supported Cooperative Work in Design

作者： Qiuyue Liu Chunxiao Wang Yinglong Wang Zhigang Zhao Shuchang Zang Huagang Wen Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Given the diversity and geographic distribution of data resources across multiple data centers, achieving unified storage and resource management presents significant challenges, complicating efforts to meet the demands for efficient cross-domain distributed data access. To address these challenges, this study proposes a cross-domain heterogeneous storage cluster architecture. Leveraging Alluxio as the foundational storage layer for data-intensive applications, this architecture facilitates unified management across various storage systems. For the storage system selection problem, this paper adopts a multi-objective approach with minimum cost, maximum reliability, shortest access distance, and load balancing as the target functions. An improved NSGA-III algorithm, incorporating generalized inverse learning and adaptive evolutionary mechanisms, is proposed for intelligently selecting the optimal storage scheme. The reasonableness and effectiveness of the improved algorithm are validated through a comparative analysis of the convergence and diversity metrics of the original and improved NSGA-III algorithms on benchmark problems, along with assessments of each objective's adaptability in real-world application scenarios.

关键词： Measurement Data centers Costs Federated learning Distributed databases Optimization methods computer architecture Load management Resource management Reliability

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