Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images

Zhou, Qing; Zhou, Zhengxin; Chen, Chunmiao; Fan, Guohua; Chen, Guang-Qiang; Heng, Haiyan; Ji, Jiansong; Dai, Yakang

doi:10.1016/j.compbiomed.2019.01.026

Cited by 38 publications

(27 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In prostate cancer, staging is achieved using the Gleason score, a combination of two scores measuring prevalence of tumor cells in two distinct locations on a slide. DNNs have shown promising initial results in predicting Gleason scores from histopathology images of prostate tumors (21,22). Nagpal and colleagues used WSI for H&E-stained prostatectomy specimens to train and test a DNN (Inception-V3) and k-nearest-neighbor classifier-based model to predict Gleason scores (21).…”

Section: Cancer Staging and Gradingmentioning

confidence: 99%

“…The group reported an improved prediction accuracy of Gleason scores estimated from their model (0.70) compared with those determined by a panel of 29 independent pathologists (0.61). Cancer staging can also be done from radiology images: Zhou and colleagues developed a deep learning approach (based on SENet and DenseNet) to predict grade (low versus high) from the MRI images of patients with liver cancer and reported an AUC of 0.83 (22). Overall, these studies indicate promising application of AI to cancer staging, with reported performance on par with trained experts despite modest AUC.…”

Section: Cancer Staging and Gradingmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

301

153

View full text Add to dashboard Cite

Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

show abstract

Section: Cancer Staging and Gradingmentioning

confidence: 99%

Section: Cancer Staging and Gradingmentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

301

153

View full text Add to dashboard Cite

show abstract

“…DenseNet-161 has demonstrated a superb performance for ILSVRC ImageNet classification task [ 43 ]. Moreover, DenseNet-161 has shown a great success in several histopathological image analysis pipelines [ 10 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In order to supply the patch-wise feature extractor network with image patches, we extract a number of patches k based on the following equation [ 7 ]:

where W and H are width and height dimensions of the input image, respectively.…”

Section: Proposed 3e-net Modelmentioning

confidence: 99%

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy

Abdelsamea

Mohamed

et al. 2021

Entropy

View full text Add to dashboard Cite

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

show abstract

“…Pathologists could provide limited information regarding cancer reorganization from normal liver tissue and assess its histopathological grade via visual inspection, but it still lacks the underlying biological differences in HCC gene mutations associated with overall survival. The recent advances in artificial intelligence (AI) provided a novel way to assist clinicians to classify medical information and images [14][15][16][17] . Recently, Lin et al 18 used multiphoton microscopy with deep learning in the automated classification of HCC differentiation.…”

Section: Introductionmentioning

confidence: 99%

“…npj Precision Oncology (2020)14 Published in partnership with The Hormel Institute, University of Minnesota1234567890():,;…”

mentioning

confidence: 99%

Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning

et al. 2020

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and assessing its histopathological grade requires visual inspection by an experienced pathologist. In this study, the histopathological H&E images from the Genomic Data Commons Databases were used to train a neural network (inception V3) for automatic classification. According to the evaluation of our model by the Matthews correlation coefficient, the performance level was close to the ability of a 5-year experience pathologist, with 96.0% accuracy for benign and malignant classification, and 89.6% accuracy for well, moderate, and poor tumor differentiation. Furthermore, the model was trained to predict the ten most common and prognostic mutated genes in HCC. We found that four of them, including CTNNB1, FMN2, TP53, and ZFX4, could be predicted from histopathology images, with external AUCs from 0.71 to 0.89. The findings demonstrated that convolutional neural networks could be used to assist pathologists in the classification and detection of gene mutation in liver cancer.

show abstract

Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images

Cited by 38 publications

References 17 publications

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial Intelligence in Cancer Research and Precision Medicine

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning

Contact Info

Product

Resources

About