Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique

Martínez-Más, José; Bueno-Crespo, Andrés; Martínez‐España, Raquel; Remezal-Solano, Manuel; Ortiz‐González, Ana; Ortiz-Reina, Sebastián; Martínez-Cendán, Juan-Pedro

doi:10.1016/j.eswa.2020.113707

Cited by 28 publications

(12 citation statements)

References 33 publications

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“…Neither transfer learning nor finetuning were performed in these tests. Apart from state-of-the-art works [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 32 , 33 , 34 , 35 ] pretrained in several medical image datasets and ImageNet, our proposal received no extra training and only took the training partition of each presented dataset. Our source code is available on .…”

Section: Resultsmentioning

confidence: 99%

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NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

et al. 2021

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The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

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Section: Resultsmentioning

confidence: 99%

“…CNNs are the most popular type of DL algorithms and the most used for medical image diagnosis. We can find several works from segmentation of lesions [ 13 , 14 , 15 ] to classification of different diseases [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

et al. 2021

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“…In Martínez-Mas et al [ 64 ], the authors propose a novel method for the early detection of cervical cancer, which is one of those with high mortality in women. Frequently, the automatic classification of medical images does not pre-clean the images to remove overlaps, which does not reflect the reality of the images obtained directly from the medical samples.…”

Section: Application Of ML Approaches In Cancer Casesmentioning

confidence: 99%

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Banegas-Luna

Peña‐García

Iftene

et al. 2021

IJMS

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Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

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“…In recent years, the development of neural network technology has led to the application of computer-assisted cervical cytology screening to receive increasing attention. For example, image classification of cervical Pap smears 26 – 29 and target detection and classification of cervical Pap smears 30 have been implemented. Commercialized products have even been developed and approved by the U.S. Food and Drug Administration 31 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Zhu

Ong

et al. 2021

Nat Commun

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Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

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Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique

Cited by 28 publications

References 33 publications

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

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