Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images | Paper Citation Record

Observation d07c6d2d-a374-45b8-a99d-d313659124e7 · outbound

This paper cites The cancer genome atlas liver hepatocellular carcinoma collection (TCGA-LIHC),.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images The cancer genome atlas liver hepatocellular carcinoma collection (TCGA-LIHC),

Reference 1

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This paper cites Livernet: efficient and robust deep learning model for automatic diagnosis of sub- types of liver hepatocellular carcinoma cancer from h&e stained liver histopathology images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Livernet: efficient and robust deep learning model for automatic diagnosis of sub- types of liver hepatocellular carcinoma cancer from h&e stained liver histopathology images,

Reference 2

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Observation a5dd0910-0ad5-469d-88e5-d6c8e98c1a23 · outbound

This paper cites Hepatocellular carcinoma: epi- demiology and molecular carcinogenesis,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Hepatocellular carcinoma: epi- demiology and molecular carcinogenesis,

Reference 3

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This paper cites Key statistics about liver cancer.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Key statistics about liver cancer

Reference 4

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Observation dc19cd2e-9c10-4120-b5c3-7e569989d390 · outbound

This paper cites Global, regional and national burden of primary liver cancer by subtype,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Global, regional and national burden of primary liver cancer by subtype,

Reference 5

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Observation 2b0742b0-fcb8-4499-9d8d-1b08dc522b7a · outbound

This paper cites Staged detection–identification framework for cell nuclei in histopathology images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Staged detection–identification framework for cell nuclei in histopathology images,

Reference 6

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Observation 6cde9785-2c3d-4f48-92ed-a5886db23b29 · outbound

This paper cites Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism,

Reference 7

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Observation c9ece36e-951e-4b56-87eb-75b03a9aa29b · outbound

This paper cites Clinical impact and frequency of anatomic pathology errors in cancer diagnoses,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Clinical impact and frequency of anatomic pathology errors in cancer diagnoses,

Reference 8

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This paper cites Diagnostic concordance among pathologists interpreting breast biopsy specimens,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Diagnostic concordance among pathologists interpreting breast biopsy specimens,

Reference 9

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Observation 7b624461-7be5-4b75-8a04-97714b41d65d · outbound

This paper cites Error reduction in surgical pathology,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Error reduction in surgical pathology,

Reference 10

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Observation 1ddd4ed4-4aab-4407-8884-045c132f12d1 · outbound

This paper cites Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,

Reference 11

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Observation 122295c8-dfea-4d5c-93d8-ba6c1247a492 · outbound

This paper cites Deep learning- based classification of liver cancer histopathology images using only global labels,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Deep learning- based classification of liver cancer histopathology images using only global labels,

Reference 12

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Observation 5dc1c64f-6162-4213-8e4d-6bd165a04d2d · outbound

This paper cites Classification and mutation prediction based on histopathology h&e images in liver cancer using deep learning,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Classification and mutation prediction based on histopathology h&e images in liver cancer using deep learning,

Reference 13

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Observation c4f5add2-bc29-4de6-abfd-442760526aa6 · outbound

This paper cites Support-vector networks,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Support-vector networks,

Reference 14

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This paper cites Imagenet classification with deep convolutional neural networks,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Imagenet classification with deep convolutional neural networks,

Reference 15

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Observation fe4986ad-36a5-4168-a954-7e3b1ace3d08 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Very deep convolutional networks for large-scale image recognition,

Reference 16

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This paper cites Deep residual learning for image recognition,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Deep residual learning for image recognition,

Reference 17

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This paper cites Densely connected convolutional networks,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Densely connected convolutional networks,

Reference 18

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This paper cites EfficientNet: Rethinking model scaling for con- volutional neural networks,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images EfficientNet: Rethinking model scaling for con- volutional neural networks,

Reference 19

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This paper cites BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer,

Reference 20

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This paper cites A deep learning method for breast cancer classification in the pathology images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images A deep learning method for breast cancer classification in the pathology images,

Reference 21

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This paper cites Deep learning vs. traditional computer vision,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Deep learning vs. traditional computer vision,

Reference 22

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Observation b9d90fdc-0bba-415d-a5c5-4da748f43b06 · outbound

This paper cites Grading of hcc biopsy images using nucleus and texture features,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Grading of hcc biopsy images using nucleus and texture features,

Reference 23

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Observation a5fa18c0-6ff9-42d9-841d-a7af86e31854 · outbound

This paper cites Dca-daffnet: An end-to-end network with deformable fusion attention and deep adaptive feature fusion for laryn- geal tumor grading from histopathology images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Dca-daffnet: An end-to-end network with deformable fusion attention and deep adaptive feature fusion for laryn- geal tumor grading from histopathology images,

Reference 24

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This paper cites How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset,

Reference 25

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This paper cites Breast cancer histology images classification: Training from scratch or transfer learning?,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Breast cancer histology images classification: Training from scratch or transfer learning?,

Reference 26

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This paper cites Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images,

Reference 27

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This paper cites A study on cnn transfer learning for image classification,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images A study on cnn transfer learning for image classification,

Reference 28

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This paper cites Automated classification of histopathology images using transfer learning,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Automated classification of histopathology images using transfer learning,

Reference 29

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This paper cites Application of deep transfer learning for automated brain abnormality classification using MR images,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Application of deep transfer learning for automated brain abnormality classification using MR images,

Reference 30

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This paper cites Lesion classification of coronary artery cta images based on cbam and transfer learning,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Lesion classification of coronary artery cta images based on cbam and transfer learning,

Reference 31

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This paper cites How transferable are features in deep neural networks?,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images How transferable are features in deep neural networks?,

Reference 32

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This paper cites Multi-phase fine-tuning: A new fine-tuning approach for sign language recognition,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Multi-phase fine-tuning: A new fine-tuning approach for sign language recognition,

Reference 33

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Observation 3dd8ba35-0e24-4e5c-8d15-24b68095d106 · outbound

This paper cites Convolutional neural networks for medical image analysis: Full training or fine tuning?,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Convolutional neural networks for medical image analysis: Full training or fine tuning?,

Reference 34

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Observation 1c06fab7-5089-44a5-96e9-eff12b79f12a · outbound

This paper cites A method for normalizing histology slides for quantitative analysis,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images A method for normalizing histology slides for quantitative analysis,

Reference 35

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This paper cites Lung and colon cancer histopathological image dataset (lc25000),.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Lung and colon cancer histopathological image dataset (lc25000),

Reference 36

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This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Pytorch: An imperative style, high- performance deep learning library,

Reference 37

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This paper cites Sgdr: Stochastic gradient descent with warm restarts,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Sgdr: Stochastic gradient descent with warm restarts,

Reference 38

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This paper cites A weakly supervised method with colorization for nuclei segmentation using point annotations,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images A weakly supervised method with colorization for nuclei segmentation using point annotations,

Reference 39

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Observation 6e7ae1da-f4b0-4a34-8370-09afdae4cf54 · outbound

This paper cites Metrolog- ical characterization of a cadx system for the classification of breast masses in mammograms,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Metrolog- ical characterization of a cadx system for the classification of breast masses in mammograms,

Reference 40

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Observation b8dca1e6-d950-4e31-8bc4-eead1cf27d99 · outbound

This paper cites A dataset for breast cancer histopathological image classification,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images A dataset for breast cancer histopathological image classification,

Reference 41

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This paper cites Deep feature selection using adaptive β-hill climbing aided whale optimization algorithm for lung and colon cancer detection,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Deep feature selection using adaptive β-hill climbing aided whale optimization algorithm for lung and colon cancer detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:50.971157Z

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Observation 47c12a0d-67bb-4168-b053-79c87f6af600 · outbound

This paper cites Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning,.

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning,

Reference 43

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