activity
20202023
most citedDeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques

322 citations · 1.1k across the 22 of their papers we have counts for

collaborators

22 papers

eess.IV2023★ 57 cited

Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning

Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they pro…

cs.CV2023★ 1 cited

ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos

Ao Chen, Jinghua Zhang, Md Mamunur Rahaman +6

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 reso…

cs.CV2022

Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features

Frank Kulwa, Chen Li, Marcin Grzegorzek +3

The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This…

cs.CV2022★ 137 cited

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

Haoyuan Chen, Chen Li, Xiaoyan Li +8

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 classificati…

cs.CV2022

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

Wanli Liu, Chen Li, Ning Xu +9

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an i…

cs.CV2022

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

Jiawei Zhang, Xin Zhao, Tao Jiang +7

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end con…

eess.IV2022★ 1 cited

EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation

Weiming Hu, Chen Li, Xiaoyan Li +9

Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is…

cs.CV2022

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

Jiawei Zhang, Chen Li, Md Mamunur Rahaman +6

With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Micro…

cs.CV2022★ 4 cited

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Xiaoqi Li, Haoyuan Chen, Chen Li +6

In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopatho…

cs.CV2021★ 2 cited

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Peng Zhao, Chen Li, Md Mamunur Rahaman +7

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict re…

cs.CV2021★ 4 cited

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

Peng Zhao, Chen Li, Md Mamunur Rahaman +5

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has stil…

cs.CV2021★ 3 cited

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

Weiming Hu, Chen Li, Xiaoyan Li +9

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. Histopatholog…

cs.CV2021★ 3 cited

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers

Wanli Liu, Chen Li, Md Mamunur Rahamana +8

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many error…

cs.CV2021★ 1 cited

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Pingli Ma, Chen Li, Md Mamunur Rahaman +5

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection m…

cs.CV2021★ 239 cited

GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection

Haoyuan Chen, Chen Li, Ge Wang +9

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…

eess.IV2021★ 12 cited

A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches

Jiawei Zhang, Chen Li, Md Mamunur Rahaman +6

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techni…

cs.CV2021★ 10 cited

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches

Chen Li, Xintong Li, Md Rahaman +8

With the development of computer-aided diagnosis (CAD) and image scanning technology, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. There…

cs.CV2021★ 20 cited

EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks

Zihan Li, Chen Li, Yudong Yao +7

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (…

eess.IV2021★ 322 cited

DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques

Md Mamunur Rahaman, Chen Li, Yudong Yao +4

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smea…

cs.CV2021★ 1 cited

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

Yixin Li, Xinran Wu, Chen Li +7

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. The…

cs.CV2020★ 70 cited

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

Yixin Li, Chen Li, Xiaoyan Li +7

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of co…

eess.IV2020★ 220 cited

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman +6

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role…