TITLE: GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer 

ABSTRACT: Background and Objective: Gastric cancer has turned out to be the fifth most
common cancer globally, and early detection of gastric cancer is essential to
save lives. Histopathological examination of gastric cancer is the gold
standard for the diagnosis of gastric cancer. However, computer-aided
diagnostic techniques are challenging to evaluate due to the scarcity of
publicly available gastric histopathology image datasets. Methods: In this
paper, a noble publicly available Gastric Histopathology Sub-size Image
Database (GasHisSDB) is published to identify classifiers' performance.
Specifically, two types of data are included: normal and abnormal, with a total
of 245,196 tissue case images. In order to prove that the methods of different
periods in the field of image classification have discrepancies on GasHisSDB,
we select a variety of classifiers for evaluation. Seven classical machine
learning classifiers, three Convolutional Neural Network classifiers, and a
novel transformer-based classifier are selected for testing on image
classification tasks. Results: This study performed extensive experiments using
traditional machine learning and deep learning methods to prove that the
methods of different periods have discrepancies on GasHisSDB. Traditional
machine learning achieved the best accuracy rate of 86.08% and a minimum of
just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest
was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions:
To the best of our knowledge, it is the first publicly available gastric cancer
histopathology dataset containing a large number of images for weakly
supervised learning. We believe that GasHisSDB can attract researchers to
explore new algorithms for the automated diagnosis of gastric cancer, which can
help physicians and patients in the clinical setting.
Background and Objective: Gastric cancer has turned out to be the fifth most
common cancer globally, and early detection of gastric cancer is essential to
save lives. Histopathological examination of gastric cancer is the gold
standard for the diagnosis of gastric cancer. However, computer-aided
diagnostic techniques are challenging to evaluate due to the scarcity of
publicly available gastric histopathology image datasets. Methods: In this
paper, a noble publicly available Gastric Histopathology Sub-size Image
Database (GasHisSDB) is published to identify classifiers' performance.
Specifically, two types of data are included: normal and abnormal, with a total
of 245,196 tissue case images. In order to prove that the methods of different
periods in the field of image classification have discrepancies on GasHisSDB,
we select a variety of classifiers for evaluation. Seven classical machine
learning classifiers, three Convolutional Neural Network classifiers, and a
novel transformer-based classifier are selected for testing on image
classification tasks. Results: This study performed extensive experiments using
traditional machine learning and deep learning methods to prove that the
methods of different periods have discrepancies on GasHisSDB. Traditional
machine learning achieved the best accuracy rate of 86.08% and a minimum of
just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest
was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions:
To the best of our knowledge, it is the first publicly available gastric cancer
histopathology dataset containing a large number of images for weakly
supervised learning. We believe that GasHisSDB can attract researchers to
explore new algorithms for the automated diagnosis of gastric cancer, which can
help physicians and patients in the clinical setting.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
http://arxiv.org/abs/2106.02473v6