Xiaoping Li
5 Papers
Xiaoping Li is an academic researcher. The author has contributed to research in topics: Identification (biology) & Cytometry. The author has an hindex of 1, co-authored 1 publications.
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Papers
Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning
Daniel Pirone,Lu Xin,Vittorio Bianco,Lisa Miccio,Wen Xiao,Leiping Che,Xiaoping Li,Pasquale Memmolo,Feng Pan,Pietro Ferraro +9 more
- 01 Nov 2022
TL;DR: In this paper , the authors used digital holographic flow cytometry to collect images of flowing cells and reconstructed their 3D tomographic phase and extracted meaningful morphometric features from the 3D and 2D phase maps through machine learning methods and finally compared their classification performance.
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Label-free cells classification in holographic flow cytometry through unbiased learning strategy
Gioele Ciaparrone,Daniel Pirone,Pierpaolo Fiore,Lu Xin,Wen Xiao,Xiaoping Li,Francesco Bardozzo,Vittorio Bianco,Lisa Miccio,Feng Pan,Pasquale Memmolo,Roberto Tagliaferri,Pietro Ferraro +12 more
TL;DR: A combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features are proposed.
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Classification of drug-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning
Lu Xin,Wen Xiao,Huanzhi Zhang,Xiaosu Yi,Xiaoping Li,Feng Pan +5 more
- 11 Aug 2023
TL;DR: Several machine learning algorithms were used to perform multi-classification on the extracted morphological features of four types of cells, and the SHapley Additive exPlanations (SHAP) method was employed to interpret the classification model.
Artificial Intelligence for Label-free cells classification in holographic microscopy
Pierpaolo Fiore,Daniel Pirone,Francesco Bardozzo,Lu Xin,Wen Xiao,Xiaoping Li,Gioele Ciaparrone,Vittorio Bianco,Lisa Miccio,Feng Pan,Pasquale Memmolo,Pietro Ferraro,Roberto Tagliaferri +12 more
TL;DR: This study applies Artificial Intelligence to classify label-free cells in holographic microscopy, leveraging digital holography's ability to image biological specimens without exogenous agents, while addressing potential bias in classification.
Classification of Paclitaxel-resistant Ovarian Cancer Cells Using Holographic Flow Cytometry through Interpretable Machine Learning
Lu Xin,Yakun Liu,Xiaoping Li,Pietro Ferraro,Feng Pan +4 more
- 01 May 2024
TL;DR: Researchers employed holographic flow cytometry and machine learning to classify paclitaxel-resistant ovarian cancer cells with over 90% accuracy, identifying key morphological features associated with drug resistance using SHapley Additive exPlanations (SHAP).