| 1. |
- Christensen, Gustav Boelsgaard, et al.
(författare)
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Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin
- 2021
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Ingår i: Skin Research and Technology. - : Wiley. - 0909-752X .- 1600-0846.
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Tidskriftsartikel (refereegranskat)abstract
- Background: The quest for diagnostic tools for the detection of cutaneous malignant melanoma (cMM) is ongoing. A challenge in cMM care is not overlooking cMM at an early stage, while simultaneously avoiding unnecessary biopsies or excisions of benign pigmented skin lesions (PSLs). A novel hyperspectral imaging (HSI) device is shown to have potential for differentiating equivocal PSLs in Asian skin types. Our objective was to assess the accuracy of the HSI device in distinguishing between cMM and benign PSLs in patients with Caucasian skin types. Methods: Patients with Caucasian skin types (Fitzpatrick I-II), enrolled for excisional biopsies of PSLs were included and examined using the HSI device. The discrimination index (DI) was used to demonstrate the sensitivity (SE) and specificity (SP) in comparison with the re-evaluated histopathology diagnoses. Results: In 186 patients, 202 pigmented skin lesions were included. The sensitivity to detect cMM was 96.7% (87/90), and the specificity for benign lesions was 42.1% (45/107). The AUC was 0.800 (95% confidence interval (CI): 0.740-0.861). Conclusions: Our novel HSI device showed a high sensitivity in detecting malignant lesions in patients with Caucasian skin types. Compared with analogous technologies, as multispectral imaging or electrical impedance spectroscopy, our device showed similar or better accuracy in differentiating cMM from benign PSLs. Therefore, it might be a useful clinical tool in skin types I-IV and where further triage of pigmented skin lesions is important.
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| 2. |
- Christensen, Gustav Boelsgaard, et al.
(författare)
-
Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin.
- 2021
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Ingår i: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI). - : Wiley-Blackwell. - 1600-0846. ; 27:5, s. 803-809
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Tidskriftsartikel (refereegranskat)abstract
- The quest for diagnostic tools for the detection of cutaneous malignant melanoma (cMM) is ongoing. A challenge in cMM care is not overlooking cMM at an early stage, while simultaneously avoiding unnecessary biopsies or excisions of benign pigmented skin lesions (PSLs). A novel hyperspectral imaging (HSI) device is shown to have potential for differentiating equivocal PSLs in Asian skin types. Our objective was to assess the accuracy of the HSI device in distinguishing between cMM and benign PSLs in patients with Caucasian skin types.Patients with Caucasian skin types (Fitzpatrick I-II), enrolled for excisional biopsies of PSLs were included and examined using the HSI device. The discrimination index (DI) was used to demonstrate the sensitivity (SE) and specificity (SP) in comparison with the re-evaluated histopathology diagnoses.In 186 patients, 202 pigmented skin lesions were included. The sensitivity to detect cMM was 96.7% (87/90), and the specificity for benign lesions was 42.1% (45/107). The AUC was 0.800 (95% confidence interval (CI): 0.740-0.861).Our novel HSI device showed a high sensitivity in detecting malignant lesions in patients with Caucasian skin types. Compared with analogous technologies, as multispectral imaging or electrical impedance spectroscopy, our device showed similar or better accuracy in differentiating cMM from benign PSLs. Therefore, it might be a useful clinical tool in skin types I-IV and where further triage of pigmented skin lesions is important.
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| 3. |
- Hirano, Ginji, et al.
(författare)
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Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet
- 2020
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Ingår i: Skin Research and Technology. - : Wiley. - 0909-752X .- 1600-0846. ; 26:6, s. 891-897
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Tidskriftsartikel (refereegranskat)abstract
- Background: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning. Materials and Methods: HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a “Mini Network” layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network. Results and Conclusion: The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions.
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| 4. |
- Kato, Kana, et al.
(författare)
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Performance improvement of automated melanoma diagnosis system by data augmentation
- 2020
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Ingår i: Advanced Biomedical Engineering. - : Japanese Society for Medical and Biological Engineering. - 2187-5219. ; 9, s. 62-70
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Tidskriftsartikel (refereegranskat)abstract
- Color information is an important tool for diagnosing melanoma. In this study, we used a hyper-spectral imager (HSI), which can measure color information in detail, to develop an automated melanoma diagnosis system. In recent years, the effectiveness of deep learning has become more widely accepted in the field of image recognition. We therefore integrated the deep convolutional neural network with transfer learning into our system. We tried data augmentation to demonstrate how our system improves diagnostic performance. 283 melanoma lesions and 336 non-melanoma lesions were used for the analysis. The data measured by HSI, called the hyperspectral data (HSD), were converted to a single-wavelength image averaged over plus or minus 3 nm. We used GoogLeNet which was pre-trained by ImageNet and then was transferred to analyze the HSD. In the transfer learning, we used not only the original HSD but also artificial augmentation dataset to improve the melanoma classification performance of GoogLeNet. Since GoogLeNet requires three-channel images as input, three wavelengths were selected from those single-wavelength images and assigned to three channels in wavelength order from short to long. The sensitivity and specificity of our system were estimated by 5-fold cross-val-idation. The results of a combination of 530, 560, and 590 nm (combination A) and 500, 620, and 740 nm (com-bination B) were compared. We also compared the diagnostic performance with and without the data augmentation. All images were augmented by inverting the image vertically and/or horizontally. Without data augmentation, the respective sensitivity and specificity of our system were 77.4% and 75.6% for combination A and 73.1% and 80.6% for combination B. With data augmentation, these numbers improved to 79.9% and 82.4% for combination A and 76.7% and 82.2% for combination B. From these results, we conclude that the diagnostic performance of our system has been improved by data augmentation. Furthermore, our system suc-ceeds to differentiate melanoma with a sensitivity of almost 80%.
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