Deep learning for dermatologists: Part II. Current applications

Puri, Pranav; Comfere, Nneka I.; Drage, Lisa A.; Shamim, Huma; Bezalel, Spencer A.; Pittelkow, Mark R.; Davis, Mark D.P.; Wang, Michael; Mangold, Aaron R.; Tollefson, Megha M.; Lehman, Julia S.; Meves, Alexander; Yiannias, James A.; Otley, Clark C.; Carter, Rickey E.; Sokumbi, Olayemi; Hall, Matthew; Bridges, Alina G.; Murphree, Dennis H.

doi:10.1016/j.jaad.2020.05.053

Cited by 38 publications

(20 citation statements)

References 41 publications

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“…Deep learning (DL) is a rapidly evolving subcategory of machine learning and is especially valuable in medical image analysis 6 . DL has been shown to be successful in performing several classification tasks, such as diagnosing skin lesions 7 , analyzing retinal images 8 , classifying chest radiography abnormalities 9,10 , and reading neural images 11,12 . One obstacle for developing DL algorithms for medical image analysis is obtaining large-scale annotations of medical images, which is often labor-intensive and requires special expertise.…”

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confidence: 99%

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Cheng

Wang²,

Chen

et al. 2021

Nat Commun

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Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960–0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948–0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912–0.936), 0.908 (95% CI, 0.885–0.908), and 0.932 (95% CI, 0.919–0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.

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confidence: 99%

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Cheng

Wang²,

Chen

et al. 2021

Nat Commun

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“…Analysis of digitized β3 integrin IHC slides relied on the well‐established H score concept 12 and was automated by a commercially available, reliable, and widely used software package. More contemporary and advanced machine learning approaches might have generated better results 16‐18 …”

Section: Discussionmentioning

confidence: 99%

β3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma

et al. 2020

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“…Deep learning algorithms were used for image classification [56] and regression analyses [34,52,57]. The addition of more data during deep learning improves the performance of the model, and this makes this technique superior to other learning techniques, such as ANNs, SVM, and RF, which reach a plateau in performance after a certain quantity of data is fed into the model.…”

Section: Dnn-mlp Model Deploymentmentioning

confidence: 99%

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

et al. 2021

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The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R2 = 0.934, NSE = 0.933, RMSE = 0.028 g/cm2, and MAE of 0.017 g/cm2 outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability.

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Deep learning for dermatologists: Part II. Current applications

Cited by 38 publications

References 41 publications

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

β3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

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