Basal cell carcinoma detection in full field OCT images using convolutional neural networks

Mandache, Diana; Dalimier, Eugénie; Durkin, John; Boceara, C.; Olivo‐Marin, Jean‐Christophe; Meas-Yedid, Vannary

doi:10.1109/isbi.2018.8363689

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…The average number of used images in the selected studies was around 7800. The lowest number of images used was 40 [ 24 ], whereas the highest number of images used was 129,450 [ 23 ]. We categorized these data set sizes into three groups, depending on the number of images used.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

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Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

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“…An immediate next step will be to demonstrate that the model can robustly generate H&E-like features of superficial basal-cell carcinoma (BCC), including cancer margins, from OCT images. This is a favourable test case, as BCC clusters are typically hundreds of microns in size 21,35 , putting them well within the range of our system. As a preliminary result, we added a few cancer samples to our training set and show feasibility for non-invasive BCC detection (Fig.…”

Section: Discussionmentioning

confidence: 99%

OCT2Hist: Non-Invasive Virtual Biopsy Using Optical Coherence Tomography

Winetraub

Yuan²,

Terem³

et al. 2021

Preprint

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Histological haematoxylin and eosin stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumour margin detection, and disease diagnosis1. Producing H&E sections, however, is invasive and time consuming. Non invasive optical imaging modalities, such as optical coherence tomography (OCT), permit label free, micron scale 3D imaging of biological tissue microstructure with significant depth (up to 1mm) and large fields of view2, but are difficult to interpret and correlate with clinical ground truth without specialized training3. Here we introduce the concept of a virtual biopsy, using generative neural networks to synthesize virtual H&E sections from OCT images. To do so we have developed a novel technique, optical barcoding, which has allowed us to repeatedly extract the 2D OCT slice from a 3D OCT volume that corresponds to a given H&E tissue section, with very high alignment precision down to 25 microns. Using 1,005 prospectively collected human skin sections from Mohs surgery operations of 71 patients, we constructed the largest dataset of H&E images and their corresponding precisely aligned OCT images, and trained a conditional generative adversarial network4 on these image pairs. Our results demonstrate the ability to use OCT images to generate high fidelity virtual H&E sections and entire 3D H&E volumes. Applying this trained neural network to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

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“…However, as static FFOCT imaging is faster, it could be used as a first row of interpretation and drive the selection of the ROIs where to apply D-FF-OCT for further investigation. For future studies, we aim to develop a first CNN trained on static FF-OCT image of the whole sample [41] to automatically define a few ROIs where the D-FF-OCT will be acquired to increase the accuracy of the diagnosis. We can also expect that progress in camera technologies and in real time processing of the D-FF-OCT image via GPU computing will help reducing the imaging time and will soon enable to record the D-FF-OCT of the entire sample in a few minutes.…”

Section: Discussionmentioning

confidence: 99%

Automatic diagnosis and biopsy classification with dynamic Full-Field OCT and machine learning

Thouvenin

Scholler

Mandache

et al. 2021

Preprint

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The adoption of emerging imaging technologies in the medical community is often hampered if they provide a new unfamiliar contrast that requires experience to be interpreted. Here, in order to facilitate such integration, we developed two complementary machine learning approaches, respectively based on feature engineering and on convolutional neural networks (CNN), to perform automatic diagnosis of breast biopsies using dynamic full field optical coherence tomography (D-FF-OCT) microscopy. This new technique provides fast, high resolution images of biopsies with a contrast similar to H&E histology, but without any tissue preparation and alteration. We conducted a pilot study on 51 breast biopsies, and more than 1,000 individual images, and performed standard histology to obtain each biopsy diagnosis. Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level, and above 96% at the biopsy level. Finally, we proposed different strategies to narrow down the spatial scale of the automatic segmentation in order to be able to draw the tumor margins by drawing attention maps with the CNN approach, or by performing high resolution precise annotation of the datasets. Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis.

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Basal cell carcinoma detection in full field OCT images using convolutional neural networks

Cited by 22 publications

References 8 publications

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Artificial Intelligence for Skin Cancer Detection: Scoping Review

OCT2Hist: Non-Invasive Virtual Biopsy Using Optical Coherence Tomography

Automatic diagnosis and biopsy classification with dynamic Full-Field OCT and machine learning

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