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Clinical Inspired MRI Lesion Segmentation

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Clinical Inspired MRI Lesion Segmentation

IEEE International Symposium on Biomedical Imaging

作者： Lijun Yan Churan Wang Fangwei Zhong Yizhou Wang School of Software and Microelectronics Peking University China Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University China School of Artificial Intelligence Beijing Normal University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the subsequence feature, i.e. the difference between post contrastenhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications.

关键词： Image segmentation Pathology Sensitivity Magnetic resonance imaging Brain tumors Breast Lesions Medical diagnosis Standards Diseases

A basic macroeconomic agent-based model for analyzing monetary regime shifts

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arXiv 2022年

作者： Peters, Florian Neuberger, Doris Reinhardt, Oliver Uhrmacher, Adelinde Deparment of Economics Faculty of Economic and Social Sciences University of Rostock Rostock Germany Visual and Analytic Computing Faculty of Computer Science and Electrical Engineering University of Rostock Rostock Germany

In macroeconomics, an emerging discussion of alternative monetary systems addresses the dimensions of systemic risk in advanced financial systems. Monetary regime changes with the aim of achieving a more sustainable financial system have already been discussed in several European parliaments and were the subject of a referendum in Switzerland. However, their effectiveness and efficacy concerning macro-financial stability are not well-known. This paper introduces a macroeconomic agent-based model (MABM) in a novel simulation environment to simulate the current monetary system, which may serve as a basis to implement and analyze monetary regime shifts. In this context, the monetary system affects the lending potential of banks and might impact the dynamics of financial crises. MABMs are predestined to replicate emergent financial crisis dynamics, analyze institutional changes within a financial system, and thus measure macro-financial stability. The used simulation environment makes the model more accessible and facilitates exploring the impact of different hypotheses and mechanisms in a less complex way. The model replicates a wide range of stylized economic facts, including simplifying assumptions to reduce model complexity. © 2022, CC BY.

关键词： Finance

(Image Present) ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection

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arXiv 2024年

作者： Sun, Zhihao Jiang, Haoran Chen, Haoran Cao, Yixin Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science Fudan University China

(Image Present) Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, in this work, we propose ForgerySleuth, which leverages M-LLMs to perform comprehensive clue fusion and generate segmentation outputs indicating specific regions that are tampered with. Moreover, we construct the ForgeryAnalysis dataset through the Chain-of-Clues prompt, which includes analysis and reasoning text to upgrade the image manipulation detection task. A data engine is also introduced to build a larger-scale dataset for the pre-training phase. Our extensive experiments demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in generalization, robustness, and explainability. The resource is available at https://***/sunzhihao18/ForgerySleuth. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

A Survey on Video Diffusion Models

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arXiv 2023年

作者： Xing, Zhen Feng, Qijun Chen, Haoran Dai, Qi Hu, Han Xu, Hang Wu, Zuxuan Jiang, Yu-Gang Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University China Microsoft Research Asia China Noah's Ark Lab China

The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not only in image generation and editing, but also in the realm of video-related research. However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain. To address this gap, this paper presents a comprehensive review of video diffusion models in the AIGC era. Specifically, we begin with a concise introduction to the fundamentals and evolution of diffusion models. Subsequently, we present an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks. We conduct a thorough review of the literature in these three key areas, including further categorization and practical contributions in the field. Finally, we discuss the challenges faced by research in this domain and outline potential future developmental trends. A comprehensive list of video diffusion models studied in this survey is available at https://***/ChenHsing/Awesome-Video-Diffusion-Models. Copyright © 2023, The Authors. All rights reserved.

关键词： Video analysis

Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems

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Dynamic Graph Neural Networks-Based Alert Link Prediction fo...

IEEE International Conference on Automated Software Engineering (ASE)

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert $a$ would lead to the occurrence of alert $b$ . Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

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Unlocking Textual and visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image

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arXiv 2024年

作者： Jiao, Pengkun Zhao, Na Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore

Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pre-trained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios. © 2024, CC BY.

关键词： Object detection

Automatic Segmentation and Alignment of Uterine Shapes from 3D Ultrasound Data

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computers in Biology and Medicine 2024年 178卷 108794-108794页

作者： Boneš, Eva Gergolet, Marco Bohak, Ciril Lesar, Žiga Marolt, Matija University of Ljubljana Faculty of Computer and Information Science Večna pot 113 Ljubljana1000 Slovenia University of Ljubljana Faculty of Medicine Vrazov trg 2 Ljubljana1000 Slovenia King Abdullah University of Science and Technology Visual Computing Center Thuwal23955-6900 Saudi Arabia

Background: The uterus is the most important organ in the female reproductive system. Its shape plays a critical role in fertility and pregnancy outcomes. Advances in medical imaging, such as 3D ultrasound, have significantly improved the exploration of the female genital tract, thereby enhancing gynecological healthcare. Despite well-documented data for organs like the liver and heart, large-scale studies on the uterus are lacking. Existing classifications, such as VCUAM and ESHRE/ESGE, provide different definitions for normal uterine shapes but are not based on real-world measurements. Moreover, the lack of comprehensive datasets significantly hinders research in this area. Our research, part of the larger NURSE study, aims to fill this gap by establishing the shape of a normal uterus using real-world 3D vaginal ultrasound scans. This will facilitate research into uterine shape abnormalities associated with infertility and recurrent miscarriages. Methods: We developed an automated system for the segmentation and alignment of uterine shapes from 3D ultrasound data, which consists of two steps: automatic segmentation of the uteri in 3D ultrasound scans using deep learning techniques, and alignment of the resulting shapes with standard geometrical approaches, enabling the extraction of the normal shape for future analysis. The system was trained and validated on a comprehensive dataset of 3D ultrasound images from multiple medical centers. Its performance was evaluated by comparing the automated results with manual annotations provided by expert clinicians. Results: The presented approach demonstrated high accuracy in segmenting and aligning uterine shapes from 3D ultrasound data. The segmentation achieved an average Dice similarity coefficient (DSC) of 0.90. Our method for aligning uterine shapes showed minimal translation and rotation errors compared to traditional methods, with the preliminary average shape exhibiting characteristics consistent with expert finding

关键词： Alignment

TraceStream: Anomalous Service Localization based on Trace Stream Clustering with Online Feedback

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TraceStream: Anomalous Service Localization based on Trace S...

International Symposium on Software Reliability Engineering (ISSRE)

作者： Tong Zhou Chenxi Zhang Xin Peng Zhenghui Yan Pairui Li Jianming Liang Haibing Zheng Wujie Zheng Yuetang Deng School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Tencent Inc. China

Modern large-scale service-based systems such as microservice systems have become increasingly complex, making it hard to localize anomalous services when various issues emerge. Traces record the workflows of requests through service instances and have been widely used in anomaly detection and root cause analysis. Existing trace-based approaches widely use statistical methods or learning-based techniques to detect trace anomalies and localize anomalous services. However, these approaches often suffer from the concept drift problem, i.e., the statistical properties of traces change over time in unforeseen ways. In this paper, we propose TraceStream, an anomalous service localization approach based on trace data stream clustering. TraceStream uses data stream clustering to discover potential anomalous trace clusters in evolving trace data and uses spectrum analysis to localize anomalous services based on the clusters. Moreover, TraceStream can effectively incorporate the online feedback of operation engineers based on the trace clusters to improve the accuracy for localizing anomalous services. Our evaluation confirms that TraceStream can effectively detect anomalies and localize anomalous services in an evolving microservice system. It can effectively incorporate human feedback to further improve the performance of anomalous service localization. Moreover, TraceStream is efficient and its efficiency can be further improved by sampling a small portion of traces by cluster.

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Learning Photometric Feature Transform for Free-Form Object Scan

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IEEE Transactions on visualization and computer Graphics 2024年 PP卷 PP页

作者： Feng, Xiang Kang, Kaizhang Pei, Fan Ding, Huakeng You, Jinjiang Tan, Ping Zhou, Kun Wu, Hongzhi Zhejiang University State Key Lab of CAD & CG Hangzhou310058 China King Abdullah University of Science and Technology Visual Computing Center Thuwal23955-6900 Saudi Arabia The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong

We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques. © 1995-2012 IEEE.

关键词： Photometry