LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&amp;E stained liver histopathology images

Aatresh, Anirudh Ashok; Alabhya, Kumar; Lal, Shyam; Kini, Jyoti R.; Saxena, PU Prakash

doi:10.1007/s11548-021-02410-4

Cited by 49 publications

(51 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to clinical variables, the predictive performance of the radiomics model in the study was similar to that of our linear and logistic models but was lower than that of the SVM, RF, and GBM models, which revealed that the optimal feature selection is important for building a precise model. Unlike the engineered features model, the DL model as a novel method for image classification has been widely used in liver cancers (37)(38)(39). A DL model based on CT and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) seemed efficient for predicting microvascular invasion in HCC (40).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Peng

Huang

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

ObjectivesWe aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE.MethodsThree hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated.ResultsGood predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each P < 0.001). The highest accuracies were achieved by integrating DL and the random forest algorithm in the training and validation cohorts (AUC = 0.995 and 0.994, respectively).ConclusionThe radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Unlike the engineered features model, the DL model as a novel method for image classification has been widely used in liver cancers ( 37 – 39 ). A DL model based on CT and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) seemed efficient for predicting microvascular invasion in HCC ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Peng

Huang

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Table 7 shows the results for both WSI datasets. For the experiments, we used the following five deep encoders of well-known CNN models: ResNet-50 [ 44 ], Inception-V3 [ 45 ], DenseNet-121 [ 46 ], MobileNet [ 47 ], RuneCNN [ 48 ], BreastNet [ 49 ], LiverNet [ 50 ], and HCNN [ 51 ]. We report the accuracy of all the experiments on five-fold cross-validation.…”

Section: Performance Resultsmentioning

confidence: 99%

Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis

et al. 2022

View full text Add to dashboard Cite

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features. The pre-trained latent vectors are extracted from each autoencoder, and these fused feature representations are utilized for classification. We observed that training with additional descriptors helps the model to overcome the limitations of multiple variants and the intricate cellular structure of histopathology data by various experiments. Our model outperforms existing state-of-the-art approaches by achieving the highest accuracies of 87.2 for ICIAR2018, 94.6 for Dartmouth, and other significant metrics for public benchmark datasets. Our model does not rely on a specific set of pre-trained features based on classifiers to achieve high performance. Unsupervised spaces are learned from the number of independent multiple descriptors and can be used with different variants of classifiers to classify cancer diseases from whole-slide images. Furthermore, we found that the proposed model classifies the types of breast and lung cancer similar to the viewpoint of pathologists by visualization. We also designed our whole-slide image processing toolbox to extract and process the patches from whole-slide images.

show abstract

“…In recent years, multiple studies have generated AI models for classifying, segmenting and diagnosing tissue from HCC samples. [29] , [30] , [31] Li et al. published a CNN-based DL algorithm that was able to grade HCC nuclei on liver histopathology, while Lal et al.…”

Section: Ai In Liver Histopathologymentioning

confidence: 99%

Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

et al. 2022

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

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

Cited by 49 publications

References 20 publications

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis

Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

Contact Info

Product

Resources

About