Min Li
Xinjiang University
16 Papers
1 Citations
Min Li is an academic researcher from Xinjiang University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 6 publications.
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Papers
Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images
Min Li,Xiaojian Ma,Chen Chen,Yushuai Yuan,Shuailei Zhang,Ziwei Yan,Cheng Chen,Fangfang Chen,Yujie Bai,Panyun Zhou,Xiaoyi Lv,Mingrui Ma +11 more
TL;DR: Wang et al. as mentioned in this paper proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma(SCLC).
Classification of Cervical Biopsy Images Based on LASSO and EL-SVM
TL;DR: This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM) and the generalization ability of the classifier was evaluated.
HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism
Panyun Zhou,Yanzhen Cao,Min Li,Yuhua Ma,Cheng Chen,Xiaojing Gan,Jianying Wu,Xiaoyi Lv,Cheng Chen +8 more
TL;DR: Wang et al. as mentioned in this paper constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and mccBAM, called HCCANet.
Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images
Min Li,Xiaohan Nie,Yilidan Reheman,Pan Huang,Shuailei Zhang,Yushuai Yuan,Chen Chen,Ziwei Yan,Cheng Chen,Xiaoyi Lv,Wei Han +10 more
TL;DR: The experimental results prove that the proposed comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learning-support vector machine (EL-SVM) and computed tomography (CT) images is feasible and promising for clinical applications.
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Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer.
Qinggang Zeng,Cheng Chen,Cheng Chen,Haitao Song,Min Li,Junyi Yan,Xiaoyi Lv +6 more
TL;DR: In this paper , a fast and low-cost diagnosis method based on serum Raman spectroscopy and deep learning algorithms was proposed for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer and healthy controls.
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