[1] Apoptosis in cancer: From pathogenesis to treatment. J. Exp. Clin. Cancer Res., 30, 87(2011).
[2] Classification of cell death: Recommendations of the nomenclature committee on cell death 2009. Cell Death Differ., 16, 3-11(2009).
[3] Apoptosis, oncosis, and necrosis: An overview of cell death. Am. J. Pathol., 146, 3-15(1995).
[4] High-content analysis-based sensitivity prediction and novel therapeutics screening for c-Met-addicted glioblastoma. Cancers, 13, 372(2021).
[5] Application of a high-content screening assay utilizing primary human lung fibroblasts to identify antifibrotic drugs for rapid repurposing in COVID-19 patients. SLAS Discov. Adv. Sci. Drug Discov., 26, 1091-1106(2021).
[6] Live-cell high content screening in drug development. Methods Mol. Biol., 1683, 149-164(2018).
[7] Past, present, and future of high content screening and the field of cellomics. Methods Mol. Biol., 356, 3-18(2007).
[8] Identification of oral squamous cell carcinoma in optical coherence tomography images based on texture features. J. Innov. Opt. Health Sci., 14, 2140001(2021).
[9] A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images. J. Innov. Opt. Health Sci., 14, 1-9(2021).
[10] Deep learning applications in medical image analysis. IEEE Access, 6, 9375-9389(2018).
[11] Searching for MobileNetV3. Proc. IEEE/CVF Int. Conf. Computer Vision, 1314-1324(2019).
[12] Imagenet classification with deep convolutional neural networks. Commun. ACM, 60, 84-90(2017).
[13] Deep residual learning for image recognition. Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 770-778(2016).
[14] Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process., 21, 1465-1477(2012).
[15] Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC-3, 610-621(1973).
[16] Automated classification of apoptosis in phase contrast microscopy using capsule network. IEEE Trans. Med. Imaging, 39, 1-10(2020).
[17] Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques. Apoptosis, 23, 290-298(2018).
[18] Apoptosis assessment in high-content and high-throughput screening assays. Biotechniques, 70, 309-318(2021).
[19] A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Sci. Rep., 6, 1-12(2016).
[20] Defining external factors that determine neuronal survival, apoptosis and necrosis during excitotoxic injury using a high content screening imaging platform. PLoS One, 12, 1-19(2017).
[21] Induction of decision trees. Mach. Learn., 1, 81-106(1986).
[22] Histograms of oriented gradients for human detection. 2013 IEEE Conf. Computer Vision and Pattern Recognition, 886-893(2005).
[23] W4: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell., 22, 809-830(2000).
[24] A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9, 62-66(1979).
[25] Machine learning in medicine: A practical introduction. BMC Med. Res. Methodol., 19, 64(2019).
[26] A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11, 674-693(1989).
[27] A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215(2020).
[28] Image denoising review: From classical to state-of-the-art approaches. Inf. Fusion, 55, 220-244(2020).
[29] ThunderSVM: A fast SVM library on GPUs and CPUs. J. Mach. Learn. Res., 19, 797-801(2018).
