A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

Wu, Haijing; Yin, Heng; Chen, Haipeng; Sun, Moyuan; Liu, Xiaoqing; Yu, Yao; Tang, Yang; Long, Hai; Zhang, Bo; Zhang, Jing; Zhou, Ying; Li, Yaping; Zhang, Guiyuing; Zhang, Peng; Zhan, Yi; Liao, Jingyi; Luo, Shuaihantian; Xiao, Rong; Y, Su; Zhao, Juanjuan; Wang, Fei; Zhang, Wei; Jin, Zhang; Lu, Qianjin

doi:10.21037/atm.2020.04.39

Cited by 72 publications

(40 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al showed that multi-lesion skin disease can be classified at the level of single-lesion skin disease, on highly selected image material (13).…”

Section: Discussionmentioning

confidence: 99%

“…This model achieved a 67-75% sensitivity in diagnosing multiple-lesion skin disease (including acne, eczema, and psoriasis). Recently Wu et al did show an impressive 95% overall diagnostic accuracy in classifying atopic dermatitis, eczema and psoriasis on selected image material (13). Studies comparing the accuracy of CAD models to clinicians are generally based on image classification equivalent to retrospective analysis, though some head to head studies were conducted with prospective collected image material (12,14,15).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

et al. 2020

View full text Add to dashboard Cite

Background: Diagnosis of skin diseases is often challenging and computer-aided diagnostic tools are urgently needed to underpin decision making. Objective: To develop a convolutional neural network model to classify clinically relevant selected multiple-lesion skin diseases, this in accordance to the STARD guidelines. Methods: This was an image-based retrospective study using multi-task learning for binary classification. A VGG-16 model was trained on 16,543 non-standardized images. Image data was distributed in training set (80%), validation set (10%), and test set (10%). All images were collected from a clinical database of a Danish population attending one dermatological department. Included was patients categorized with ICD-10 codes related to acne, rosacea, psoriasis, eczema, and cutaneous t-cell lymphoma. Results: Acne was distinguished from rosacea with a sensitivity of 85.42% CI 72.24-93.93% and a specificity of 89.53% CI 83.97-93.68%, cutaneous t-cell lymphoma was distinguished from eczema with a sensitivity of 74.29% CI 67.82-80.05% and a specificity of 84.09% CI 80.83-86.99%, and psoriasis from eczema with a sensitivity of 81.79% CI 78.51-84.76% and a specificity of 73.57% CI 69.76-77.13%. All results were based on the test set. Conclusion: The performance rates reported were equal or superior to those reported for general practitioners with dermatological training, indicating that computer-aided diagnostic models based on convolutional neural network may potentially be employed for diagnosing multiple-lesion skin diseases.

show abstract

“…Wu et al showed that multi-lesion skin disease can be classified at the level of single-lesion skin disease, on highly selected image material (13).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Our model was based on the recently proposed EfficientNet that achieved better accuracy and efficiency (6.1 times faster) with a smaller number of model parameters (8.4 times less) than other networks [23]. It has been applied to solve medical problems, such as the diagnosis of COVID-19 [24,25] and the classification of skin diseases [26]. EfficientNet utilizes a new scaling method, so-called compound coefficient, to systematically balance the depth (d: the length of the model), width (w: the number of channels) and resolution (r: the image size) of a given network.…”

Section: Effeicient-b0 Networkmentioning

confidence: 99%

Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

Wu¹,

Supanich²,

Deng³

2021

JoAIMS

View full text Add to dashboard Cite

Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure timely treatment. In this study, we developed an ensembled deep neural network for the detection and subtype classification of intracranial hemorrhage. The model consisted of two parallel network pathways, one using three different window level/width settings to enhance the image contrast of brain, blood, and soft tissue. The other extracted spatial information of adjacent image slices to the target slice. Both pathways exploited the EfficientNet-B0 as the basic architecture and were ensembled to generate the final prediction. Class activation mapping was applied in both pathways to highlight the regions of detected hemorrhage and the associated subtypes. The model was trained and tested using Intracranial Hemorrhage Detection Challenge (IHDC) dataset launched by the Radiological Society of North America (RSNA) in 2019, which contained 674,258 head noncontrasts computer tomography images acquired from 19,530 patients. An independent dataset (CQ500) acquired from another institution was used to test the generalizability of the trained model. The overall accuracy, sensitivity, and F1 score for intracranial hemorrhage detection were 95.7%, 85.9%, and 86.7% on IHDC testing dataset and 92.4%, 92.6%, and 93.4% on external CQ500 testing dataset. The heatmaps by class activation mapping successfully demonstrated discriminative feature regions of the predicted hemorrhage locations and subtypes, providing visual guidance for radiologists to assist in rapid diagnosis of intracranial hemorrhage.

show abstract

“…In a recent study, a high confusion rate of Pso with Ecz was reported given the similar appearance of these skin lesions. Therefore, the diagnostic e ciency of single-model DLS of clinical skin images may be limited by the imitating nature of LE skin lesions 21 . Given the lack of characteristic and well-directed pathological changes in LE lesions, the application of single-model DLS of HE staining images in LE may not be as effective as its application in cancer tissue 22 .…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

Yang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Lupus erythematosus (LE) has the reputation of being the Great Imitator due to its various cutaneous manifestations, which often lead to difficulties in accurate diagnosis and classification. Even with the assistance of deep learning systems (DLSs), clinical skin image-based single modalities still experience difficulty distinguishing subtypes of LE. Here, we present an application of multimodal DLS (MMDLS), in which the training database consists of clinical skin images, multi-immunohistochemistry (multi-IHC) images and the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) scores, to assist in the classification of LE subtypes (discoid LE (DLE), annular subacute cutaneous LE (A-SCLE), papulosquamous subacute cutaneous LE (P-SCLE), systemic LE (SLE)) and to aid in the differential diagnosis of other similar skin diseases. A total of 386 cases with 580 clinical skin images, 3380 multi-IHC images and 2019 EULAR/ACR scores from 25 institutions in China were included, and EfficientNet-B3 and ResNet-18 were utilized in this study. By comparing the single-modal and dual-modal DLS performances in multiple classifications, we demonstrate the superiority of the MMDLS (Ave-Sen = 0.9116, Ave-Spe = 0.9921, Ave-Pre = 0.9281). Moreover, the performance of MMDLS on 13 classifications was superior to that of dermatologists and pathologists (F1 score: dermatologists = 0.4471, pathologists = 0.6691, MMDLS = 0.9582) with an average area under the curve (AUC) of 0.9956. These results highlight the potential of the combined application of MMDLS and multi-IHC images to assist pathologists and dermatologists in diagnosing LE subtypes and similar skin diseases.

show abstract

A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

Cited by 72 publications

References 18 publications

Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis Among Lupus and Other Similar Skin Diseases

Contact Info

Product

Resources

About