SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis

Chen, Ao; Li, Chen; Zou, Shuojia; Rahaman, Md Mamunur; Yao, Yu-Dong; Chen, Haoyuan; Yang, Hechen; Zhao, Peng; Hu, Weiming; Liu, Wanli; Grzegorzek, Marcin

doi:10.1016/j.bbe.2021.12.010

Cited by 66 publications

(18 citation statements)

References 27 publications

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“…Classical machine learning methods have a rigorous theoretical foundation. Their simplified ideas can show good classification results on some specific features and algorithms ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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IntroductionGastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem.MethodsThe complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier.ResultsThe experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity.DiscussionSuitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.

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“…Classical machine learning methods have a rigorous theoretical foundation. Their simplified ideas can show good classification results on some specific features and algorithms ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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“…The continuous improvement of transformer models and multilayer perceptron (MLP) models has made them popular. Especially, deep learning methods are widely used in many biomedical image analysis tasks, such as COVID-19 idetificaion [25,26], microorganism image analysis [27,28,29], histopatholgical image analysis [30,31,32,33,34] , cytopathological image analysis [35,36,37] and sperm image analysis [38,39].…”

Section: Overview Of Deep Learning Methodsmentioning

confidence: 99%

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Chen,

Li,

et al. 2022

Preprint

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In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.

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“…A sperm microscopic video data set is released in our previous work [54] and it is used for the ex-periments of this paper. These sperm microscopic videos in the data set are obtained by a WLJY-9000 computer-aided sperm analysis system [55] under a 20× objective lens and a 20× electronic eyepiece.…”

Section: Experimental Settings 411 Data Setmentioning

confidence: 99%

TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos

Zou¹,

Li²,

Sun³

et al. 2022

Preprint

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The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, > 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving 85.60% AP 50 in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.

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SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis

Cited by 66 publications

References 27 publications

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos

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