Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning DOI Creative Commons

Ming Ping Yong,

Yan Chai Hum, Khin Wee Lai

и другие.

IEEE Access, Год журнала: 2023, Номер 12, С. 1434 - 1457

Опубликована: Дек. 15, 2023

Cancer is a leading global cause of death. Histopathology image analysis widely recognized as the gold standard for cancer diagnosis, playing crucial role in early detection and reducing mortality rates. However, this diagnostic task performed manually by pathologists, to high human errors variabilities due huge number images screen tissue complexities. With emergence deep learning, specifically Convolutional Neural Network (CNN), computer-aided there growing interest from medical community automate labor-intensive manual screening. Despite its promising performance, learning models still encounter challenges when it comes extracting comprehensive histopathological features optimal results. To tackle issue, our study introduces based on intra-domain transfer ensemble learning. We evaluated these public histopathology datasets, including Gastric Sub-size Image Database (GasHisSDB), Chaoyang colorectal, Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Carcinoma (CPTAC-CCRCC). Our achieved state-of-the-art accuracy: 99.78% GasHisSDB, 85.69% Chaoyang, 99.17% CPTAC-CCRCC. These results highlight models' ability extract rich perform well low-resolution images. Thus, have potential assist reduce their workload, enhance patient survival

Язык: Английский

A comprehensive survey on applications of transformers for deep learning tasks DOI
Saidul Islam, Hanae Elmekki,

Ahmed Elsebai

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 241, С. 122666 - 122666

Опубликована: Ноя. 23, 2023

Язык: Английский

Процитировано

140
A survey of the vision transformers and their CNN-transformer based variants DOI
Asifullah Khan,

Zunaira Rauf,

Anabia Sohail

и другие.

Artificial Intelligence Review, Год журнала: 2023, Номер 56(S3), С. 2917 - 2970

Опубликована: Окт. 4, 2023

Язык: Английский

Процитировано

98
LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset DOI
Pan Huang, Hualiang Xiao, Peng He

и другие.

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2024, Номер 28(6), С. 3557 - 3570

Опубликована: Март 8, 2024

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, channels, and class activation which caused serious impact accuracy interpretability of LSCC grading. While traditional transformer block makes extensive use parameter attention, model overlearns information, resulting ineffectively reducing proportion semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), improve ability to learn high-effect target reduce Firstly, according generalized linear probabilistic, demonstrate that (LA) has stronger highly effective than attention. Secondly, first-type LA LA-ViT utilizes map position subspace realize query. Then, it uses channel key, adopts average convergence obtain value. And those construct mechanism. Thus, reduces semantics maps channels. Thirdly, second-type probability matrix decision level weight key query, respectively. So, maps. Finally, build new complex pathology image dataset address problem, less research grading models because lacking meaningful datasets. After experiments, whole metrics outperform other state-of-the-art methods, visualization match better regions interest pathologists' decision-making. Moreover, experimental results conducted public show superior generalization performance methods.

Язык: Английский

Процитировано

20
W-Net: A boundary-enhanced segmentation network for stroke lesions DOI
Zelin Wu, Xueying Zhang,

Fenglian Li

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 230, С. 120637 - 120637

Опубликована: Июнь 1, 2023

Язык: Английский

Процитировано

33
A survey of Transformer applications for histopathological image analysis: New developments and future directions DOI Creative Commons
Chukwuemeka Clinton Atabansi, Jing Nie, Haijun Liu

и другие.

BioMedical Engineering OnLine, Год журнала: 2023, Номер 22(1)

Опубликована: Сен. 25, 2023

Transformers have been widely used in many computer vision challenges and shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage capturing long-range contextual information learning more complex relations image data, applied to histopathological processing tasks. In this survey, we make an effort present a thorough analysis uses analysis, covering several topics, from newly built Transformer models unresolved challenges. To be precise, first begin by outlining fundamental principles attention mechanism included other key frameworks. Second, analyze Transformer-based applications imaging domain provide evaluation 100 research publications across different downstream tasks cover most recent innovations, including survival prediction, segmentation, classification, detection, representation. Within survey work, also compare performance CNN-based techniques based on recently published papers, highlight major challenges, interesting future directions. Despite outstanding architectures number papers reviewed anticipate that further improvements exploration are still required future. We hope paper will give readers field study understanding up-to-date list summary provided at https://github.com/S-domain/Survey-Paper .

Язык: Английский

Процитировано

28
Continuous Refinement-Based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems DOI
Jia Wu,

Tian Luo,

Jiachen Zeng

и другие.

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2024, Номер 28(4), С. 2091 - 2102

Опубликована: Янв. 9, 2024

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial more quickly. The cascade structure refines segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling cropping patches during inference process due to ultra-high resolution complex images. This not only increases cost computation time but also in loss details corrupts global contextual information. study proposes Pathology Image Assistance Program (CRSDPI) medical decision-making systems that is based on continuous improvement. After locating region interest using maximum inter-class variance method, pictures are preprocessed account impacts staining inconsistencies sensitivity variations model's performance. Ultimately, we create two-phase continuously refined network (TCRNet) combining an enhanced refinement model with coarse built pyramid scene parsing network. introduces auxiliary term speed up convergence, implicit function reduce computational reconstruct details. TCRNet target successively aligning features without need take cascading decoder operations after encoder. Experiments conducted digital images breast cancer osteosarcoma demonstrate superior prediction accuracy our strategy.

Язык: Английский

Процитировано

17
Diffusion model-based text-guided enhancement network for medical image segmentation DOI
Z.-W. Dong, Genji Yuan, Zhen Hua

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 249, С. 123549 - 123549

Опубликована: Фев. 20, 2024

Язык: Английский

Процитировано

14
Vision transformer promotes cancer diagnosis: A comprehensive review DOI
Xiaoyan Jiang, Shuihua Wang‎, Yudong Zhang

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 252, С. 124113 - 124113

Опубликована: Май 1, 2024

Язык: Английский

Процитировано

9
An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images DOI Creative Commons

Majdi Khalid,

S. Deivasigamani,

V Sathiya

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 17, 2024

The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diagnosis Currently, deep learning techniques are applied to enhance cancer classification and localization in histopathological analysis. Moreover, traditional might loss integrated information while evaluating thousands patches recovered from whole slide images (WSIs). This research proposes a novel detection network (CCDNet) that combines coordinate attention transformer with atrous convolution. CCDNet first denoises input using Wiener based Midpoint weighted non-local means filter (WMW-NLM) guaranteeing precise diagnoses maintain features. Also, convolution (AConvCAT) introduced, which successfully advantages two networks classify tissue at various scales by capturing local global information. Further, model Cross-shaped window (CrSWin) tiny changes multiple angles. proposed achieved accuracy rates 98.61% 98.96%, on histological NCT-CRC-HE-100 K datasets correspondingly. comparison demonstrates suggested framework performed better than advanced methods already use. In hospitals, clinicians can use verify diagnosis.

Язык: Английский

Процитировано

9
Structure Embedded Nucleus Classification for Histopathology Images DOI
Wei Lou, Xiang Wan, Guanbin Li

и другие.

IEEE Transactions on Medical Imaging, Год журнала: 2024, Номер 43(9), С. 3149 - 3160

Опубликована: Апрель 12, 2024

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in appearance of different nuclei types cause difficulties identifying nuclei. Most neural network based methods are affected by local receptive field convolutions, and pay less attention to spatial distribution or irregular contour shape a nucleus. In this paper, we first propose novel polygon-structure feature learning mechanism that transforms nucleus into sequence points sampled order, employ recurrent aggregates sequential change distance between key obtain learnable features. Next, convert graph structure with as nodes, build embed their representations. To capture correlations categories surrounding tissue patterns, further introduce edge features defined background textures adjacent Lastly, integrate both polygon mechanisms whole framework can extract intra inter-nucleus structural characteristics classification. Experimental results show proposed achieves significant improvements compared previous methods. Code data made available via https://github.com/lhaof/SENC.

Язык: Английский

Процитировано

7