Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Vision transformers for computational histopathology
Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …
information contained in gigabyte whole slide images, aiming at providing cancer patients …
The state of the art for artificial intelligence in lung digital pathology
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of
digital pathology (DP) and an increase in computational power have led to the development …
digital pathology (DP) and an increase in computational power have led to the development …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Sam-path: A segment anything model for semantic segmentation in digital pathology
Semantic segmentations of pathological entities have crucial clinical value in computational
pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have …
pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have …
[HTML][HTML] Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey
Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …
Artificial intelligence applications in histopathology
Histopathology is a vital diagnostic discipline in medicine, fundamental to our
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
Meta multi-task nuclei segmentation with fewer training samples
Cells/nuclei deliver massive information of microenvironment. An automatic nuclei
segmentation approach can reduce pathologists' workload and allow precise of the …
segmentation approach can reduce pathologists' workload and allow precise of the …
Inter-and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
Acquiring pixel-level annotations is often limited in applications such as histology studies
that require domain expertise. Various semi-supervised learning approaches have been …
that require domain expertise. Various semi-supervised learning approaches have been …
Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study
Background There is a growing need for new improved classifiers of prognosis in
hepatocellular carcinoma (HCC) patients to stratify them effectively. Methods A deep …
hepatocellular carcinoma (HCC) patients to stratify them effectively. Methods A deep …
Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review
In the last decade, the focus of computational pathology research community has shifted
from replicating the pathological examination for diagnosis done by pathologists to …
from replicating the pathological examination for diagnosis done by pathologists to …