A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi

Tognetti, Linda; Bonechi, Simone; Andreini, Paolo; Bianchini, Monica; Scarselli, Franco; Cevenini, Gabriele; Moscarella, Elvira; Farnetani, Francesca; Longo, Caterina; Lallas, Aimilios; Carrera, Cristina; Puig, Susana; Tiodorović, Danica; Perrot, Jean; Pellacani, Giovanni; Argenziano, Giuseppe; Cinotti, Élisa; Cataldo, Gennaro; Balistreri, Alberto; Mecocci, A.; Gori, Marco; Rubegni, Pietro; Cartocci, Alessandra

doi:10.1016/j.jdermsci.2020.11.009

Cited by 34 publications

(28 citation statements)

References 31 publications

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“…These results are consistent with those of previous studies in other fields [ 13 , 15 , 16 , 25 , 26 ]. However, performance metrics other than AUC in this study were not improved in some CNN models.…”

Section: Discussionsupporting

confidence: 94%

“…Similarly, diagnostic studies using DL have reported that diagnostic accuracy is higher when the patient variables and images are combined [ 12 ]. However, most studies reported improved results when some difference was attained by simple subtraction of the diagnostic accuracies [ 13 , 14 , 15 , 16 ]. Moreover, few studies have compared the statistical methods [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

et al. 2021

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Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification.

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Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

et al. 2021

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“…In recent years, the use of Deep Learning (DL) has improved the state of the art in many different fields, ranging from computer vision [1][2][3] and text analysis [4][5][6] to bioinformatics [7,8]. More specifically, DL-based decision support systems have become increasingly popular in the medical field [9]-and in particular for application in the Internet of Medical Things-where CNNs have been successfully employed for the classification of radiological, magnetic resonance or CT (Computerized Axial Tomography) images [10,11] and for natural images, for instance, in the classification of atypical nevi and melanomas [12][13][14], for the segmentation of bacterial colonies grown on Petri plates [15,16] and for retinal images [17]. In this paper, a DL aortic image semantic segmentation system is presented.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

et al. 2021

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The automatic segmentation of the aorta can be extremely useful in clinical practice, allowing the diagnosis of numerous pathologies to be sped up, such as aneurysms and dissections, and allowing rapid reconstructive surgery, essential in saving patients’ lives. In recent years, the success of Deep Learning (DL)-based decision support systems has increased their popularity in the medical field. However, their effective application is often limited by the scarcity of training data. In fact, collecting large annotated datasets is usually difficult and expensive, especially in the biomedical domain. In this paper, an automatic method for aortic segmentation, based on 2D convolutional neural networks (CNNs), using 3D CT (computed axial tomography) scans as input is presented. For this purpose, a set of 153 CT images was collected and a semi-automated approach was used to obtain their 3D annotations at the voxel level. Although less accurate, the use of a semi-supervised labeling technique instead of a full supervision proved necessary to obtain enough data in a reasonable amount of time. The 3D volume was analyzed using three 2D segmentation networks, one for each of the three CT views (axial, coronal and sagittal). Two different network architectures, U-Net and LinkNet, were used and compared. The main advantages of the proposed method lie in its ability to work with a reduced number of data even with noisy targets. In addition, analyzing 3D scans based on 2D slices allows for them to be processed even with limited computing power. The results obtained are promising and show that the neural networks employed can provide accurate segmentation of the aorta.

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“…Other efforts have been made to identify skin cancer using algorithms such as ensembles of different models [50], [51], feature aggregation [52], [53], multi-stage CNN models [54], [55], and a combination of multimodal data [51], [56]. In addition, a new deep CNN that combined dermoscopic data with clinical data (e.g., age, sex, diameter, and body location of lesion) was developed for the subtle differential diagnosis of early melanomas from their simulator's dysplastic nevi [57]. Because deep learning models are usually considered to be uninterpretable and dermatologists are concerned with how CNN models provide predictions [58], several studies have explored constructing models in a more intuitive manner.…”

Section: Computer-aided Early Melanoma Diagnosismentioning

confidence: 99%

Early Melanoma Diagnosis with Sequential Dermoscopic Images

Yu,

Nguyen,

Nguyen

et al. 2021

Preprint

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Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single timepoint images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first followup images). These results demonstrate that our model can

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A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi

Cited by 34 publications

References 31 publications

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

Early Melanoma Diagnosis with Sequential Dermoscopic Images

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