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Multi-instance curriculum learning for histopathology image classification with bias reduction

Medical image analysis 2025年 105卷 103647页

作者： Zihao Mi Jianan Zhang Xueyu Liu Guanghui Yue Junhong Yue Mingqiang Wei Yidi Li Yongfei Wu College of Computer Science and Technology (College of Data Science) Taiyuan University of Technology Taiyuan Shanxi 030024 China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. College of Artificial Intelligence Taiyuan University of Technology Taiyuan Shanxi 030024 China. College of Computer Science and Technology (College of Data Science) Taiyuan University of Technology Taiyuan Shanxi 030024 China College of Artificial Intelligence Taiyuan University of Technology Taiyuan Shanxi 030024 China. Electronic address: wuyongfei@***.

Multi-instance learning (MIL) exhibits advanced and surpassed capabilities in understanding and recognizing complex patterns within gigapixel histopathological images. However, current MIL methods for the analysis of the histopathological images still give rise to two main concerns. On one hand, vanilla MIL methods intuitively focus on identifying salient instances (easy-to-classify instances) without considering hard-to-classify instances, which is biased and prone to produce false positive instances. On the other hand, since the positive tissue occupies only a small fraction of histopathological images, it is commonly suffer from class imbalance between positive and negative instances, causing the MIL model to overly focus on the majority class in training instances classifier. In light of these issues of bias learning, we propose a multi-instance curriculum learning method that collaboratively incorporates hard negative instance mining and positive instance augmentation to improve classification performance of the model. Specifically, we first initialize the MIL model using easy-to-classify instances, then we mine the hard negative instances (hard-to-classify instances) and augment the positive instances via the diffusion model. Finally, the MIL model is retrained with memory rehearsal method by combining the mined negative instances and the augmented positive instances. Technically, the diffusion model is first designed to generate lesion instances, which optimally augment diverse features to reflect realistic positive samples with post screening scenario. Extensive experimental results show that the proposed method alleviates model bias in MIL and improves model interpretability.

关键词： Curriculum learning Diffusion model Hard negative instance mining Histopathology image Multi-instance learning Positive instance augmentation

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Aesthetically Relevant Image Captioning

arXiv

引用

arXiv 2022年

作者： Zhong, Zhipeng Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng National Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS’s can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://***/PengZai/ARIC. © 2022, CC BY-NC-ND.

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Detecting Communities in Heterogeneous Multi-Relational Networks: A Message Passing based Approach

arXiv

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arXiv 2020年

作者： Qiao, Maoying Yu, Jun Bian, Wei Tao, Dacheng Data61 CSIRO Australia School of Computer Science Hangzhou Dianzi University China Centre for Artificial Intelligence University of Technology Sydney Australia UBTECH Sydney Artificial Intelligence Centre School of Computer Science Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Community is a common characteristic of networks including social networks, biological networks, computer and information networks, to name a few. Community detection is a basic step for exploring and analysing these network data. Typically, homogenous network is a type of networks which consists of only one type of objects with one type of links connecting them. There has been a large body of developments in models and algorithms to detect communities over it. However, real-world networks naturally exhibit heterogeneous qualities appearing as multiple types of objects with multi-relational links connecting them. Those heterogeneous information could facilitate the community detection for its constituent homogeneous networks, but has not been fully explored. In this paper, we exploit heterogeneous multi-relational networks (HMRNet) and propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks. Specifically, an HMRNet is reorganized into a hierarchical structure with homogeneous networks as its layers and heterogeneous links connecting them. To detect communities in such an HMRNet, the problem is formulated as a maximum a posterior (MAP) over a factor graph. Finally a message passing based algorithm is derived to find a best solution of the MAP problem. Evaluation on both synthetic and real-world networks confirms the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Message passing

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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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PPT Fusion: Pyramid Patch Transformer for a Case Study in Image Fusion

arXiv

引用

arXiv 2021年

作者： Fu, Yu Xu, Tianyang Wu, Xiao-Jun Kittler, Josef Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

The Transformer architecture has witnessed a rapid development in recent years, outperforming the CNN architectures in many computer vision tasks, as exemplified by the Vision Transformers (ViT) for image classification. However, existing visual transformer models aim to extract semantic information for high-level tasks, such as classification and detection. These methods ignore the importance of the spatial resolution of the input image, thus sacrificing the local correlation information of neighboring pixels. In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations. In addition, we construct a Pyramid Transformer to effectively extract the non-local information from the entire image. After obtaining a set of multi-scale, multi-dimensional, and multi-angle features of the original image, we design the image reconstruction network to ensure that the features can be reconstructed into the original input. To validate the effectiveness, we apply the proposed Patch Pyramid Transformer to image fusion tasks. The experimental results demonstrate its superior performance, compared to the state-of-the-art fusion approaches, achieving the best results on several evaluation indicators. Thanks to the underlying representational capacity of the PPT network, it can directly be applied to different image fusion tasks without redesigning or retraining the network. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

arXiv

引用

arXiv 2022年

作者： Maaroof, Bestan B. Rashid, Tarik A. Abdulla, Jaza M. Hassan, Bryar A. Alsadoon, Abeer Mohammadi, Mokhtar Khishe, Mohammad Mirjalili, Seyedali Information Technology Department College of Commerce University of Sulaimani KRI Iraq Computer Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology KRI Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani KRI Sulaymaniyah Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Sydney Australia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application. © 2022, CC BY.

关键词： Particle swarm optimization (PSO)

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Authors’ Response to Peer Reviews of “Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery data”

引用

JMIRx Med 2024年 5卷

作者： Isabella Degen Kate Robson Brown Henry W J Reeve Zahraa S Abdallah Interactive Artificial Intelligence Centre for Doctoral Training School of Computer Science Faculty of Science and Engineering University of Bristol 1 Cathedral Square College Green Bristol United Kingdom University College Dublin President's Office College of Engineering and Architecture University College Dublin Dublin Ireland School of Mathematics Faculty of Science and Engineering University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology Faculty of Science and Engineering University of Bristol Bristol United Kingdom

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DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP ...

引用

2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020

作者： Amin, Kareem Kapetanakis, Stelios Polatidis, Nikolaos Althoff, Klaus-Dieter Dengel, Andreas Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781728167992

With widespread modernization, digitization and transformations of most of industries, artificial intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture. © 2020 IEEE.

关键词： Case based reasoning

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Improved drug-target interaction prediction with Intermolecular Graph Transformer

arXiv

引用

arXiv 2021年

作者： Liu, Siyuan Wang, Yusong Wang, Tong Deng, Yifan He, Liang Shao, Bin Yin, Jian Zheng, Nanning Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Microsoft Research Asia Beijing100080 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China School of Computer Science Fudan University Shanghai200433 China

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models often neglect certain aspects of the intermolecular information, hindering the performance of prediction. We recognize this problem and propose a novel approach named Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively, and shows superior generalization ability to unseen receptor proteins. Furthermore, IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. © 2021, CC BY-NC-SA.

关键词： Proteins

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Video Is Graph: Structured graph module for video action recognition

arXiv

引用

arXiv 2021年

作者： Li, Rong-Chang Xu, Tianyang Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science the School of Internet of Things Engineering Jiangnan University P.R. Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—In the field of action recognition, video clips are always treated as ordered frames for subsequent processing. To achieve spatio-temporal perception, existing approaches propose to embed adjacent temporal interaction in the convolutional layer. The global semantic information can therefore be obtained by stacking multiple local layers hierarchically. However, such global temporal accumulation can only reflect the high-level semantics in deep layers, neglecting the potential low-level holistic clues in shallow layers. In this paper, we first propose to transform a video sequence into a graph to obtain direct long-term dependencies among temporal frames. To preserve sequential information during transformation, we devise a structured graph module (SGM), achieving fine-grained temporal interactions throughout the entire network. In particular, SGM divides the neighbors of each node into several temporal regions so as to extract global structural information with diverse sequential flows. Extensive experiments are performed on standard benchmark datasets, i.e., Something-Something V1 & V2, Diving48, Kinetics-400, UCF101, and HMDB51. The reported performance and analysis demonstrate that SGM can achieve outstanding precision with less computational complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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