/pdf/il-mcam-an-interactive-learning-and-multi-channel-attention-229cl00v.pdf

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

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields. 

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A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

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% AP50 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.

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

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TOD-CNN: An effective convolutional neural network for tiny object detection in sperm videos

Background: Breast cancer has the highest prevalence among all cancers in women globally. The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern. In computer-aided diagnosis, most traditional classification models use a single network to extract features, although this approach has significant limitations. Moreover, many networks are trained and optimized on patient-level datasets, ignoring lower-level data labels. Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions. First, the BreaKHis dataset is randomly divided into training, validation, and test sets. Then, data augmentation techniques are used to balance the numbers of benign and malignant samples. Third, based on their transfer learning performance and the complementarity between networks, VGG16, Xception, ResNet50, and DenseNet201 are selected as base classifiers. Results: In a ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of 98.90%. To verify the capabilities of our method, it is experimentally compared with the latest Transformer and multilayer perception (MLP) models on the same dataset. Our ensemble model shows a a 5%–20% advantage, emphasizing its far-reaching abilities in classification tasks. Conclusion: This research focuses on improving the performance of a classification model with an ensemble algorithm. Transfer learning has an essential role in classification of small datasets, improving training speed and accuracy. Our model outperforms many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis. 

Application of transfer learning and ensemble learning in image-level classification for breast histopathology

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

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection.

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SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection

The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey. To facilitate understanding, the methods involved are analysed in the sequence of general steps in sperm analysis. In other words, the methods related to sperm detection (localization) are first analysed, and then the methods of sperm tracking are analysed. Beside this, we analyse and prospect the present situation and future of CASA. According to our work, the feasible for applying in sperm microscopic video of methods mentioned in this review is explained. Moreover, existing challenges of object detection and tracking in microscope video are potential to be solved inspired by this survey.

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

Background With the gradual increase of infertility in the world nowadays, among which male sperm problems are the main factor of infertility, more and more couples are using Computer Assisted Sperm Analysis to assist in the analysis and treatment of infertility. Meanwhile, the rapid development of deep learning has led to strong results in image classification tasks. However, the classification of sperm images has not been well studied in current deep learning methods, and the sperm images are often disturbed by noise in practical Computer Assisted Sperm Analysis applications. The purpose of this paper is to come to investigate the anti-noise robustness of deep learning classification methods on sperm images. Methods The SVIA dataset is a publicly large-scale sperm dataset containing three subsets. In this paper, we used subset-C, which provides more than 125,000 independent images of sperms and impurities, including 121,401 sperm images and 4,479 impurity images. In order to investigate the anti-noise robustness of deep learning classification methods on sperm images. we conducted a comprehensive comparative study of sperm images using many Convolutional Neural Network and Visual Transformer deep learning methods to find the deep learning model with the most stable anti-noise robustness. Results This study proved that ViT had strong robustness for the classification of the tiny object (sperm and impurity) image datasets under some conventional noises and adversarial attacks. In particular, under the influence of Poisson noise, the accuracy changed from 91.45% to 91.08%, the precision of impurity changes from 92.7% to 91.3%, the recall of impurity changed from 88.8% to 89.5%, and the F1-score of impurity changed 90.7% to 90.4%. Meanwhile, the precision of sperm changed from 90.9% to 90.5%, the recall of sperm changed from 92.5% to 93.8%, and the F1-score of sperm changed from 92.1% to 90.4%. Conclusion We obtain two conclusions: the image classification effects of sperm images are strongly affected by noise in current deep learning methods; the robustness against noise of VT methods based on global information is stronger than that of CNN methods based on local information, indicating that the robustness against noise is mainly reflected in global information. 

A Comprehensive Comparative Study of Deep Learning Methods for Noisy Sperm Image Classification: from Convolutional Neural Network to Visual Transformer

Shuojia Zou

Papers

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

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

SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

A Comprehensive Comparative Study of Deep Learning Methods for Noisy Sperm Image Classification: from Convolutional Neural Network to Visual Transformer