A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions

Zhu, Meng; Zhao, Zhicheng; Li, Bingyang; Su, Fei; Guo, Limei

doi:10.1109/tmi.2021.3059699

Cited by 28 publications

(18 citation statements)

References 39 publications

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“…In order to develop a clinically relevant CNN model for pathologic diagnosis, a superb dataset from expert pathologists must be constructed. Recently, Meng et al provided a public cervical histopathology dataset for computer-aided diagnosis, called MTCHI [20]. Pathologic diagnosis is sometimes equivocal and might be challenging to perform in some lesions in the gray zone or lesions with reactive changes.…”

Section: Discussionmentioning

confidence: 99%

“…However, most recent studies have applied techniques for the detection and classi cation of invasive cancers [8][9][10][11][12][13] rather than intraepithelial or premalignant lesions. With regard to cervical lesions, some studies have been devoted to the creation of computer-assisted reading systems for assessing cervical cytology specimens [14], and only a limited number of studies have focused on examining CINs [15][16][17][18][19][20][21]. In this study, we aimed to develop and assess an optimal convolutional neural network (CNN) model for classi cation of CINs.…”

Section: Introductionmentioning

confidence: 99%

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Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Cho

Kim

Park

et al. 2021

Preprint

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Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1,106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the 4-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, and 0.971 in the non-neoplasm, CIN3, CIN1 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the 3-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%) and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Cho

Kim

Park

et al. 2021

Preprint

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“…2 Department of Information Science and Engineering, Northeastern University, Shen Yang, China. 3 Department of Information Science and Engineering, Northeastern University, Shen Yang, China. 4 Department of Information Science and Engineering, Northeastern University, Shen Yang, China.…”

Section: Appendix Acknowledgementsmentioning

confidence: 99%

“…The recent advancements made in deep learning have been applied to different medical fields in order to detect (at an earlier stage) or predict certain anomalies [3][4][5]. In the ophthalmological domain, digital fundus images analysis using deep learning methods have started to receive a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

MulTiNet: Multimodal Neural Networks for Glaucoma Based on Transfer Learning

Han

Zhao

et al. 2021

Preprint

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Background: Being one of the most serious causes of irreversible blindness, glaucoma has many subtypes and complex symptoms. In clinic, doctors usually need to use a variety of medical images for diagnosis. Optical Coherence Tomography (OCT), Visual Field (VF) , Fundus Photosexams (FP) and Ultrasonic BioMicroscope (UBM) are widely-used and complementary techniques for diagnosing glaucoma.Methods: At present, the field of intelligent diagnosis of glaucoma is limited by two major problems. One is the small number of data sets, and the other is the low diagnostic accuracy of Single-Modal Modal. In order to solve the above two problems, we have done the following work. First, we construct DualSY glaucoma multimodal data set. The four most important subtypes of glaucoma are discussed in this article which are Primary Open Angle Glaucoma (POAG), Primary Angle Closure Glaucoma (PACG), Primary Angle Closure Suspect (PACS) and Primary Angle Closure (PAC). Each patient in the DualSY data set contains more than five medical images, as shown in the figure 4.And DualSY are labeled with image-level multi-labels. Second, We propose a new Multi-Modal classification network for glaucoma, which is a multiclass classification model with various medical images of glaucoma patients and text information as input. The network structure consists of three main branches to deal with patient metadata, domain-based glaucoma features and medical images. Transfer learning method is introduced into this paper due to the small number of medical image data sets. The flowchart is shown in Figure 5.Result: Our method on glaucoma diagnosis outperforms state-of-the-art methods. A promising average result of overall accuracy (ACC) of 94.7% is obtained. Our data set outperformed most data sets in glaucoma diagnosis with an accuracy of 87.8%.Conclusions: The results suggest that medical images such as Heidelberg OCT and three-dimensional fundus photos used in this paper can better express the high-level information of glaucoma and our modal greatly improve the accuracy of glaucoma diagnosis. At the same time, this data set has great potential, and we continue to study this data.

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“…A key to the successful treatment of cancer is early detection [ 3 , 4 ] because of the substantial decrease in mortality that the detection of tumoral lesions and masses in the early stages of the illness can produce [ 5 , 6 , 7 ]. At the moment, early diagnosis of cancer is achieved by using a range of imaging techniques, such as Computed Tomography (CT), blue laser endoscopy, Magnetic Resonance Imaging (MRI), fluorescence molecular imaging [ 8 , 9 , 10 , 11 , 12 ], histopathology [ 13 , 14 , 15 ], and cytology [ 16 , 17 , 18 ]. Interestingly, when histopathology and cytology are employed as probing tools for determining the level of malignancy of an early-stage tumour [ 19 ], they are not used alone but in conjunction with standard imaging techniques (e.g., CT, MRI, Positron Emission Tomography (PET), and ultrasounds).…”

Section: Introductionmentioning

confidence: 99%

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

Canetta

2021

IJMS

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Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

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A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions

Cited by 28 publications

References 39 publications

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

MulTiNet: Multimodal Neural Networks for Glaucoma Based on Transfer Learning

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

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