TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set

Rubin, Moran; Stein, Omer; Turko, Nir A.; Nygate, Yoav; Roitshtain, Darina; Karako, Lidor; Barnea, Itay; Giryes, Raja; Shaked, Natan T.

doi:10.1016/j.media.2019.06.014

Cited by 104 publications

(67 citation statements)

References 38 publications

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“…In single-cell studies, phase images of cells of interest could guide laser-capture microdissection to link observed behavior, morphology, and gene expression at a single cell level. Indeed, advanced machine learning techniques, including deep learning, 29 have recently been applied to isolate cell subpopulations based on unique phase features 6 and other phenotypic differences, 66,37 including metastatic versus primary cancer 67 and different types of nonactivated lymphocytes. 68 The phase/morphology score concept described here could be applied to support decision-making in intelligent cell sorting systems, such as flow cytometry with QPI, 69,34 to partition cells from a heterogeneous population into distinct morphological groups.…”

Section: Discussionmentioning

confidence: 99%

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

et al. 2020

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Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

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Section: Discussionmentioning

confidence: 99%

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

et al. 2020

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“…In recent years, deep learning has made great progress in the field of natural images and medical images, and has achieved excellent results in classification. [16][17][18][19] Deep learning provides a unified classification framework for feature extraction, thus getting rid of troublesome manual image feature extraction. However, deep learning needs to rely on a large amount of data to build models.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [19] a hybridization method between transfer learning and GANs for the classification of healthy cells and cancer cell lines acquired by quantitative phase imaging was proposed. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

Fu¹

2020

Medical Physics

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To achieve automatic classification of viable and necrotic tumor regions in osteosarcoma, most of the existing deep learning methods can only design a simple model to prevent overfitting on small datasets, which leads to the weak ability of extracting image features and low accuracy of the models. In order to solve the above problem, a deep model with Siamese network (DS-Net) was designed in this paper. Methods: The DS-Net constructed on the basis of full convolutional networks is composed of an auxiliary supervision network (ASN) and a classification network. The construction of the ASN based on the Siamese network aims to solve the problem of a small training set (the main bottleneck of deep learning in medical images). It uses paired data as the input and updates the network through combined labels. The classification network uses the features extracted by the ASN to perform accurate classification. Results: Pathological diagnosis is the most accurate method to identify osteosarcoma. However, due to intraclass variation and interclass similarity, it is challenging for pathologists to accurately identify osteosarcoma. Through the experiments on hematoxylin and eosin (H&E)-stained osteosarcoma histology slides, the DS-Net we constructed can achieve an average accuracy of 95.1%. Compared with existing methods, the DS-Net performs best in the test dataset. Conclusions: The DS-Net we constructed can not only effectively realize the histological classification of osteosarcoma, but also be applicable to many other medical image classification tasks affected by small datasets.

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“…In other words, considering the proposed H‐SVM as a binary classifier, we are able to identify exactly MPs in pretreated seawater, thus discarding the other objects falling within the same range of characteristic scales. Previous works have proposed the use of holographic reconstructions to classify particles, cells, or microorganisms based on statistical methods or ML architectures . However, none of the existing ML‐DH approaches have tackled the problem of identifying MPs, which have their own specificity as the MP class consists of a wide heterogeneity of materials, morphologies, and characteristic scales.…”

Section: Discussionmentioning

confidence: 99%

Microplastic Identification via Holographic Imaging and Machine Learning

Bianco

Memmolo

Carcagnì

et al. 2020

Advanced Intelligent Systems

110

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Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.

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TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set

Cited by 104 publications

References 38 publications

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

Microplastic Identification via Holographic Imaging and Machine Learning

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