Research graph
References from A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles. Local targets link to admitted publications; unresolved targets remain external evidence.
Clinical actionability of molecular targets in endometrial cancer
10.1038/s41568-019-0177-x · 2019 · External reference
Adjuvant radiotherapy for stage I endometrial cancer
2012 · External reference
10.1007/978-3-030-30766-0_25
10.1007/978-3-030-30766-0_25 · 2020 · External reference
International patterns and trends in endometrial cancer incidence, 1978–2013
10.1093/jnci/djx214 · 2018 · External reference
Risk factors for endometrial cancer: An umbrella review of the literature
10.1002/ijc.31961 · 2019 · External reference
Clinical utility of endometrial cell block cytology in postmenopausal women
10.1159/000479307 · 2017 · External reference
Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning
2019 · External reference
Box C/D snoRNA SNORD89 influences the occurrence and development of endometrial cancer through 2’-O-methylation modification of Bim
10.1038/s41420-022-01102-5 · 2022 · External reference
Controversy on Positive Peritoneal Cytology of Endometrial Carcinoma
2022 · External reference
What is the best method of detecting endometrial cancer in outpatients?-endometrial sampling, suction curettage, endometrial cytology
10.1111/j.1365-2303.2007.00509.x · 2008 · External reference
Liquid-based endometrial cytology: cyto-histological correlation in a population of 917 women
10.1111/j.1365-2303.2007.00463.x · 2007 · External reference
Non-invasive urine based tests for the detection of bladder cancer
10.1136/jclinpath-2012-200812 · 2012 · External reference
Cervical cancer screening: more choices in 2019
10.1001/jama.2019.4595 · 2019 · External reference
Diagnostic accuracy of cytology for the detection of endometrial cancer in urine and vaginal samples
10.1038/s41467-021-21257-6 · 2021 · External reference
Endometrial Cytology as a Method to Improve the Accuracy of Diagnosis of Endometrial Cancer: Case Report and Meta-Analysis
2019 · External reference
Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning
10.1038/s41374-021-00537-1 · 2021 · External reference
Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape
10.1016/j.jasc.2019.03.003 · 2019 · External reference
Dermatologist-level classification of skin cancer with deep neural networks
10.1038/nature21056 · 2017 · External reference
A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells
10.1155/2021/5569458 · 2021 · External reference
Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images
10.1016/j.media.2020.101814 · 2021 · External reference
PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network
10.1109/tmi.2020.2980839 · 2020 · External reference
A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma
External reference
LiSNet: An artificial intelligence -based tool for liver imaging staging of hepatocellular carcinoma aggressiveness
10.1002/mp.15972 · 2022 · External reference
Vocal cord lesions classification based on deep convolutional neural network and transfer learning
10.1002/mp.15371 · 2022 · External reference
Automated segmentation of endometrial cancer on MR images using deep learning
10.1038/s41598-020-80068-9 · 2021 · External reference
Deep learning-based hysteroscopic intelligent examination and ultrasound examination for diagnosis of endometrial carcinoma
10.1007/s11227-021-04046-2 · 2022 · External reference
A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images
10.1186/s12938-023-01169-w · 2023 · External reference
Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists’ decisions of deep myometrial invasion
10.1016/j.mri.2021.10.024 · 2022 · External reference
Quantitative evaluation of myometrial infiltration depth ratio for early endometrial cancer based on deep learning
10.1016/j.bspc.2023.104685 · 2023 · External reference
Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study
10.3390/ijerph17165993 · 2020 · External reference
Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence
10.1371/journal.pone.0282577 · 2023 · External reference
Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts
10.1016/s2589-7500(22)00210-2 · 2023 · External reference
A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer
10.21037/qims-22-220 · 2023 · External reference
Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network
10.1038/s41598-021-93792-7 · 2021 · External reference
The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists
10.1186/s12880-022-00808-3 · 2022 · External reference
Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images
10.1007/s00432-023-04838-4 · 2023 · External reference
Clinical-grade endometrial cancer detection system via whole-slide images using deep learning
2022 · External reference
Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models
10.1016/j.xcrm.2021.100400 · 2021 · External reference
Dual-path network with synergistic grouping loss and evidence driven risk stratification for whole slide cervical image analysis
10.1016/j.media.2021.101955 · 2021 · External reference
10.1109/isbi.2019.8759152
10.1109/isbi.2019.8759152 · External reference
Cervical cytopathology image refocusing via multi-scale attention features and domain normalization
10.1016/j.media.2022.102566 · 2022 · External reference
A primer of endometrial cytology with histological correlation
10.1002/dc.20745 · 2007 · External reference
World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects
10.1001/jama.2013.281053 · 2013 · External reference
10.1007/978-3-319-24574-4_28
10.1007/978-3-319-24574-4_28 · External reference
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
10.1109/tmi.2019.2959609 · 2020 · External reference
An Encoder–Decoder Deep Learning Framework for Building Footprints Extraction from Aerial Imagery
10.1007/s13369-022-06768-8 · 2023 · External reference
10.1109/iros51168.2021.9636111
10.1109/iros51168.2021.9636111 · External reference
10.1109/cvpr.2019.00326
10.1109/cvpr.2019.00326 · External reference
SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation
10.1016/j.media.2021.102025 · 2021 · External reference
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2021 · External reference
10.1109/cvpr42600.2020.00975
10.1109/cvpr42600.2020.00975 · External reference
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2018 · External reference
10.1109/acpr.2015.7486599
10.1109/acpr.2015.7486599 · External reference
Measures of the Amount of Ecologic Association Between Species
10.2307/1932409 · 1945 · External reference
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Fully Convolutional Networks for Semantic Segmentation
10.1109/tpami.2016.2572683 · 2017 · External reference
10.1109/vcip.2017.8305148
10.1109/vcip.2017.8305148 · External reference
10.1007/978-3-030-01234-2_49
10.1007/978-3-030-01234-2_49 · External reference
10.1007/978-3-030-01234-2_49
10.1007/978-3-030-01234-2_49 · External reference
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10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · External reference
10.1109/cvpr.2016.308
10.1109/cvpr.2016.308 · External reference
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10.1109/cvpr.2017.634
10.1109/cvpr.2017.634 · External reference
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10.1109/cvpr.2017.243
10.1109/cvpr.2017.243 · External reference
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Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
10.1186/s12859-017-1685-x · 2017 · External reference
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