Multi-categorical classification using deep learning applied to the diagnosis of
            gastric cancer

Kloeckner, Jonas; Sansonowicz, Tatiana; Rodrigues, Áttila Leães; Nunes, Tatiana Wittée Neetzow

doi:10.5935/1676-2444.20200013

Cited by 11 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are several deep learning models that have been developed for the diagnostic of gastric cancers using whole slide images (11)(12)(13)15,24) and a comparison of some of these studies and ours is summarized in Supplementary Table S9. Although each study adopted a results of 0.97-0.99 AUROC in the prospective set.…”

Section: Discussionmentioning

confidence: 99%

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Park¹,

Jang

Kim³

et al. 2021

Clinical Cancer Research

View full text Add to dashboard Cite

Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool. Experimental Design: Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases. Results: Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy. Conclusions: Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens.

show abstract

Section: Discussionmentioning

confidence: 99%

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Park¹,

Jang

Kim³

et al. 2021

Clinical Cancer Research

View full text Add to dashboard Cite

show abstract

“…In [ 50 ], the authors propose a ten-layer convolutional neural network, in which three convolutional layers extract features, four pooling layers reduce image size, and three full connection layers output feature values. The workflow of this approach is shown in Figure 14 .…”

Section: Classifier Design Methodsmentioning

confidence: 99%

“… The framework of the proposed method in [ 50 ]. This figure corresponds to Figure 1 in the original paper.…”

Section: Figurementioning

confidence: 99%

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

show abstract

“…The balancing module has two types of channels: the first is a channel attention (CA) module and the second is a spatial attention (SA) module. In the study of [17], the authors propose a ten-layer convolutional neural network, in which three convolutional layers extract features, four pooling layers reduce the image size, and three fully connected layers output feature values. In the study of [18], the authors propose a recalibration-based multiinstance deep learning method for histopathological image classification of gastric cancer.…”

Section: Related Workmentioning

confidence: 99%

LGFFN-GHI: A Local-Global Feature Fuse Network for Gastric Histopathological Image Classification

Li¹,

Liu²

2022

JCC

View full text Add to dashboard Cite

Gastric cancer remains the third most common cause of cancer-related death. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, manual pathology examination is timeconsuming and laborious. Computer-aided diagnosis (CAD) systems can assist pathologists in diagnosing pathological images, thus improving the efficiency of disease diagnosis. In this paper, we propose a two-branch network named LGFFN-GHI, which can classify histopathological images of gastric cancer into two categories: normal and abnormal. LGFFN-GHI consists of two parallel networks, ResNet18 and Pvt-Tiny, which extract local and global features of microscopic gastric tissue images, respectively. We propose a feature blending module (FFB) that fuses local and global features at the same resolution in a cross-attention manner. This enables ResNet18 to acquire the global features extracted by Pvt-Tiny, while enabling Pvt-Tiny to acquire the local features extracted by ResNet18. We conducted experiments on a novel publicly available sub-size image database of gastric histopathology (Ga-sHisSDB). The experimental results show that LGFFN-GHI achieves an accuracy of 96.814%, which is 2.388% and 3.918% better than the baseline methods ResNet18 and Pvt-Tiny, respectively. Our proposed network exhibits high classification performance, demonstrating its effectiveness and future potential for the gastric histopathology image classification (GHIC) task.

show abstract

Multi-categorical classification using deep learning applied to the diagnosis of gastric cancer

Cited by 11 publications

References 0 publications

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

LGFFN-GHI: A Local-Global Feature Fuse Network for Gastric Histopathological Image Classification

Contact Info

Product

Resources

About