检索结果-内蒙古大学图书馆

UniSAL: Unified Semi-supervised Active Learning for histopathological image classification

MEDICAL image ANALYSIS 2025年 102卷 103542页

作者： Zhong, Lanfeng Qian, Kun Liao, Xin Huang, Zongyao Liu, Yang Zhang, Shaoting Wang, Guotai Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Fudan Univ Zhongshan Hosp Dept Informat & Intelligence Dev 180 Fenglin Rd Shanghai 200032 Peoples R China Sichuan Univ West China Univ Hosp 2 Dept Pathol Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Sichuan Canc Hosp & Inst Affiliated Canc Hosp Sichuan Clin Res Ctr CancDept Pathol Chengdu 610042 Peoples R China Shanghai AI Lab Shanghai 200030 Peoples R China

histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of available labeled data for training deep neural networks. To reduce human efforts and improve efficiency for annotation, we propose a Unified Semi-supervised Active Learning framework (UniSAL) that effectively selects informative and representative samples for annotation. First, unlike most existing active learning methods that only train from labeled samples in each round, dual-view high-confidence pseudo training is proposed to utilize both labeled and unlabeled images to train a model for selecting query samples, where two networks operating on different augmented versions of an input image provide diverse pseudo labels for each other, and pseudo label-guided class-wise contrastive learning is introduced to obtain better feature representations for effective sample selection. Second, based on the trained model at each round, we design novel uncertain and representative sample selection strategy. It contains a Disagreement-aware Uncertainty Selector (DUS) to select informative uncertain samples with inconsistent predictions between the two networks, and a Compact Selector (CS) to remove redundancy of selected samples. We extensively evaluate our method on three public pathological image classification datasets, i.e., CRC5000, Chaoyang and CRC100K datasets, and the results demonstrate that our UniSAL significantly surpasses several state-of-the-art active learning methods, and reduces the annotation cost to around 10% to achieve a performance comparable to full annotation. Code is available at https://***/HiLab-git/UniSAL.

关键词： Active learning Contrastive learning histopathological image classification Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

HiViT: Hierarchical attention-based Transformer for multi-scale whole slide histopathological image classification

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Yu, Jinze Li, Shuo Tan, Luxin Zhou, Haoyi Li, Zhongwu Li, Jianxin Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100083 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100083 Peoples R China Beihang Univ Sch Software Beijing 100083 Peoples R China Peking Univ Canc Hosp Inst Minist Educ Dept Pathol Key Lab Carcinogenesis & Translat Res Beijing 100142 Peoples R China

The classification of Whole Slide images (WSIs) remains a challenging task in computer-aided diagnostics. Though Multi-Instance Learning (MIL) and patch-wise modeling have become the mainstream methods in current research, they rely on a strong assumption that different patches are independent and identically distributed. Therefore, the contextual correlations within the images and across all the patches have been neglected, resulting in inferior performance, particularly in global-level tasks like mutation predictions. In this paper, we propose HiViT, a multi-scale WSI classification Transformer model utilizing the proposed Cross-scale Hierarchical Self-Attention (CHSA) mechanism to aggregate patch features among different scales and capture context information from the entire image with a reasonable memory cost. The CHSA mechanism is developed based on the multiple magnification scannings of WSIs and restricts cross-scale self-attention computation within local-constrained windows to aggregate patch features among different scales and capture context information from the entire image while getting rid of the infeasible memory cost of vanilla Transformers, and finally achieves the first fully trainable Transformer model for the WSI classification problem. Additionally, the Multi-Scale Feature Augmentation method is proposed to enhance permutation invariance on a large scale, which is important in the MIL assumption, while preserving local fine-grained contextual correlations. We extensively evaluated HiViT on several real-world datasets of diverse tasks, and our model outperformed the current state-of-the-art Transformer and MIL-based methods. Code will be available at https://***/ BUAA-SMART-Med-CV/HiViT.

关键词： Deep learning Vision Transformer histopathological image classification Medical image analysis

来源：评论

学校读者我要写书评

暂无评论

histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization

引用

APPLIED INTELLIGENCE 2019年第9期49卷 3406-3424页

作者： Pal, Raju Saraswat, Mukesh Jaypee Inst Informat Technol Comp Sci & Engn Noida Uttar Pradesh India Jaypee Inst Informat Technol Dept Comp Sci & Engn Noida Uttar Pradesh India

An exponential growth of histopathological digital images over the Internet requires an efficient method for organizing them properly for better retrieval and analysis process. For the same, an automatic histopathological image classification system can be useful. Moreover, such classification system may also be used to identify the inflamed and healthy tissues from tissue image datasets. However, complex background structures and heterogeneity among histopathological tissue images make it a complicated process. Therefore, this paper introduces an innovative method for categorization of histopathological images using an enhanced bag-of-feature framework. To obtain the optimal visual words in bag-of-features, a new spiral biogeography-based optimization algorithm has been proposed which introduces a spiral search and random search in the mutation operator to generate the suitability index variables. The efficacy of the spiral biogeography-based optimization algorithm has been tested on CEC 2017 benchmarks problems. Moreover, the applicability of the proposed classification method has been observed on two histopathological image datasets, Blue Histology image dataset and ADL histopathological image dataset. The efficacy of the spiral biogeography-based optimization algorithm based bag-of-features method has been analyzed and compared with other state-of-the-art methods with respect to average accuracy, recall, precision, and F1-measure parameters.

关键词： histopathological image classification Bag-of-features Biogeography-based optimization

来源：评论

学校读者我要写书评

暂无评论

histopathological image classification by Optimized Neural Network Using IGSA 16th

Histopathological Image Classification by Optimized Neural N...

引用

16th International Conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Mittal, Himanshu Saraswat, Mukesh Pal, Raju Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9783030369873;9783030369866

The histopathological image classification is a vivid application for medical diagnosis and neural network has been successful in the image classification task. However, finding the optimal values of the neural network is still a challenging task. To accomplish the same, this paper considers a two-layer neural network which is optimized through intelligent gravitational search algorithm. Further, the optimized two-layer neural network is applied for the histopathological tissue classification into healthy and inflamed. The proposed method is validated on the publicly available tissue dataset, namely Animal Diagnostic Laboratory (ADL). The experimental results firm the better performance of the proposed method against state-of-the-art methods in terms of seven performance measures, namely recall, specificity, precision, false negative rate (FNR), accuracy, F1-score, and G-mean.

关键词： histopathological image classification Two layer neural network Intelligent gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

histopathological image classification through discriminative feature learning and mutual information-based multi-channel joint sparse representation

引用

JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2020年 70卷 102799-102799页

作者： Li, Xiao. Tang, Hongzhong Zhang, Dongbo. Liu, Ting. Mao, Lizhen. Chen, Tianyu. Xiangtan Univ Coll Informat Engn Xiangtan 411105 Peoples R China Natl Univ Def Technol Changsha 410022 Peoples R China Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Peoples R China

histopathological image classification is a very challenging task because of the biological heterogeneities and rich geometrical structures. In this paper, we propose a novel histopathological image classification framework, which includes the discriminative feature learning and the mutual information-based multi-channel joint sparse representation. We first propose a stack-based discriminative prediction sparse decomposition (SDPSD) model by incorporating the class labels information to predict deep discriminant features automatically. Subsequently, a mutual information-based multi-channel joint sparse model (MIMCJSM) is presented to jointly encode the common component and particular components of the discriminative features. Especially, the main advantage of the MIMCJSM is the construction of a joint dictionary using a mutual information criterion, which contains a common sub-dictionary and three particular sub-dictionaries. Based on the joint dictionary, the MIMCJSM captures the relationship of multi-channel features, which can improve discriminative ability of joint sparse representation coefficients. Finally, the joint sparse representation coefficients of different levels can be aggregated using the spatial pyramid matching (SPM) model, and the linear support vector machine (SVM) is used as the classifier. Experimental results on ADL and BreaKHis datasets demonstrate that our proposed framework consistently performs better than popular existing classification frameworks. Additionally, it can show promising strong-robustness performance for histopathological image classification. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Discriminative feature learning Stack-based discriminative prediction sparse decomposition (SDPSD) Mutual information-based Multi-channel joint sparse model (MIMJSM) histopathological image classification

来源：评论

学校读者我要写书评

暂无评论

histopathological image classification Using Discriminative Feature-Oriented Dictionary Learning

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2016年第3期35卷 738-751页

作者： Vu, Tiep Huu Mousavi, Hojjat Seyed Monga, Vishal Rao, Ganesh Rao, U. K. Arvind Penn State Univ Dept Elect Engn University Pk PA 16802 USA Penn State Univ Dept Elect Engn University Pk PA 16802 USA Univ Texas MD Anderson Canc Ctr Dept Neurosurg Houston TX 77002 USA Univ Texas MD Anderson Canc Ctr Dept Bionformat & Computat Biol Houston TX 77002 USA

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

关键词： histopathological image classification sparse coding dictionary learning feature extraction cancer grading

来源：评论

学校读者我要写书评

暂无评论

CoMHisP: A Novel Feature Extractor for histopathological image classification Based on Fuzzy SVM With Within-Class Relative Density

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第1期29卷 103-117页

作者： Kumar, Abhinav Singh, Sanjay Kumar Saxena, Sonal Singh, Amit Kumar Shrivastava, Sameer Lakshmanan, K. Kumar, Neeraj Singh, Raj Kumar BHU Indian Inst Technol Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India NIT Patna Dept Comp Sci & Engn Patna 800005 Bihar India Indian Vet Res Inst ICAR Div Vet Biotechnol Bareilly 243122 Uttar Pradesh India Deemed Univ Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Asia Univ Dept CS & IE Taichung 41354 Taiwan

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% (+/- 1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.

关键词： Feature extraction Tumors Support vector machines image analysis Breast cancer Uncertainty Canine mammary tumors (CMTs) center of mass (CoM) feature engineering fuzzy membership fuzzy support vector machine with relative density histopathological image classification

来源：评论

学校读者我要写书评

暂无评论

Coherence-regularized discriminative dictionary learning for histopathological image classification

引用

SIGNAL image AND VIDEO PROCESSING 2019年第5期13卷 923-931页

作者： Tang, Hongzhong Li, Xiao Zhang, Xiaogang Zhang, Dongbo Mao, Lizhen Liu, Ting Xiangtan Univ Coll Informat Engn Xiangtan 411105 Hunan Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Hunan Peoples R China

In this paper, a novel coherence-regularized discriminative dictionary learning (CRDDL) algorithm is proposed to deal with histopathological image classification. By incorporating two constructed special regularization terms into an objective function, i.e., the self-coherence within each intra-class dictionary and the mutual coherence between inter-class dictionaries, high-quality discriminative healthy and diseased dictionaries can both be explicitly learned. Furthermore, to balance the reconstruction and discrimination abilities of learned dictionaries, we minimize the reconstruction error of intra-class samples and maximize the reconstruction error of inter-class samples. Finally, reconstruction error vectors are employed to design the classifier of histopathological images. Experimental results demonstrate the improved performance of the proposed CRDDL algorithm in comparison with other previously reported discriminative dictionary learning algorithms.

关键词： Discriminative dictionary learning Self-coherence within intra-class dictionary Mutual coherence between inter-class dictionaries histopathological image classification

来源：评论

学校读者我要写书评

暂无评论

Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2021年第1期59卷 153-164页

作者： Tang, Hongzhong Mao, Lizhen Zeng, Shuying Deng, Shijun Ai, Zhaoyang Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang Peoples R China Xiangtan Univ Coll Automat & Elect Informat Xiangtan Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Hunan Univ Inst Biophys Linguist Coll Foreign Languages Changsha Hunan Peoples R China

histopathological image contains rich pathological information that is valued for the aided diagnosis of many diseases such as cancer. An important issue in histopathological image classification is how to learn a high-quality discriminative dictionary due to diverse tissue pattern, a variety of texture, and different morphologies structure. In this paper, we propose a discriminative dictionary learning algorithm with pairwise local constraints (PLCDDL) for histopathological image classification. Inspired by the one-to-one mapping between dictionary atom and profile, we learn a pair of discriminative graph Laplacian matrices that are less sensitive to noise or outliers to capture the locality and discriminating information of data manifold by utilizing the local geometry information of category-specific dictionaries rather than input data. Furthermore, graph-based pairwise local constraints are designed and incorporated into the original dictionary learning model to effectively encode the locality consistency with intra-class samples and the locality inconsistency with inter-class samples. Specifically, we learn the discriminative localities for representations by jointly optimizing both the intra-class locality and inter-class locality, which can significantly improve the discriminability and robustness of dictionary. Extensive experiments on the challenging datasets verify that the proposed PLCDDL algorithm can achieve a better classification accuracy and powerful robustness compared with the state-of-the-art dictionary learning methods.

关键词： Discriminative dictionary learning Local geometry information Graph-based pairwise local constraints histopathological image classification

来源：评论

学校读者我要写书评

暂无评论

Label-Efficient Breast Cancer histopathological image classification

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2019年第5期23卷 2108-2116页

作者： Qi, Qi Li, Yanlong Wang, Jitian Zheng, Han Huang, Yue Ding, Xinghao Rohde, Gustavo Kunde Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Fujian Peoples R China Univ Virginia Dept Biomed Engn Charlottesville VA 22908 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22908 USA

The automatic classification of breast cancer histopathological images has great significance in computer-aided diagnosis. Recently, deep learning via neural networks has enabled pattern detection and prediction using large, labeled datasets;whereas, collecting and annotating sufficient histological data using professional pathologists is time consuming, tedious, and extremely expensive. In the proposed paper, a deep active learning framework is designed and implemented for classification of breast cancer histopathological images, with the goal of maximizing the learning accuracy from very limited labeling. This method involves manual annotation of the most valuable unlabeled samples, which are then integrated into the training set. The model is then iteratively updated with an increasing training set. Here, two selection strategies are discussed for the proposed deep active learning framework: An entropy-based strategy and a confidence-boosting strategy. The proposed method has been validated using a publicly available breast cancer histopathological image dataset, wherein each image patch is binarily classified as benign or malignant. The experimental results demonstrate that, compared with a random selection, our proposed framework can reduce annotation costs up to 66.67%, with higher accuracy and less expensive annotation than standard query strategy.

关键词： Breast cancer histopathological image classification deep learning active learning query strategy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：