As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection.
no code implementations • 16 Aug 2023 • Zhiyu Ma, Chen Li, Tianming Du, Le Zhang, Dechao Tang, Deguo Ma, Shanchuan Huang, Yan Liu, Yihao Sun, Zhihao Chen, Jin Yuan, Qianqing Nie, Marcin Grzegorzek, Hongzan Sun
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
no code implementations • 1 Dec 2022 • Liyu Shi, Xiaoyan Li, Weiming Hu, HaoYuan Chen, Jing Chen, Zizhen Fan, Minghe Gao, Yujie Jing, Guotao Lu, Deguo Ma, Zhiyu Ma, Qingtao Meng, Dechao Tang, Hongzan Sun, Marcin Grzegorzek, Shouliang Qi, Yueyang Teng, Chen Li
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet.
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
Cervical cytopathology image classification is an important method to diagnose cervical 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.
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments.
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy.
This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
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.
Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis.
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.
Each type of EM contains 40 original and 40 GT images, in total 1680 EM images.
no code implementations • 11 Oct 2021 • Hechen Yang, Chen Li, Xin Zhao, Bencheng Cai, Jiawei Zhang, Pingli Ma, Peng Zhao, Ao Chen, Hongzan Sun, Yueyang Teng, Shouliang Qi, Tao Jiang, Marcin Grzegorzek
The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ". XML" format file.
Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it.
In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification.
We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
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.
In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks.
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
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.
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
Finally, the application prospect of the analytical method in this field is discussed.
In this article, we have studied the development of microorganism counting methods using digital image analysis.
In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms.
This paper reviews the methods of WSI analysis based on machine learning.