This document summarizes a study on developing a deep learning system (DLS) for differential skin disease diagnosis using teledermatology data. The DLS was trained on a dataset of 14,000 skin disease cases labeled by 43 dermatologists. It achieved average sensitivity of 80% on validation data, outperforming dermatologists and other medical professionals. Subgroup analysis found the DLS was better at distinguishing malignant, infectious, and non-infectious diseases requiring different treatments. Integrated gradients helped explain the model's decisions. Clinical metadata, like self-reported symptoms, also improved performance. In conclusion, the DLS shows promise as a diagnostic tool for common skin diseases.

1. A Deep Learning Approach to Antibiotic Discovery
PR-246
주성훈, Samsung SDS
2020. 5. 10.
A deep learning system for differential diagnosis of skin diseases
Google Health

2. 1. Research Background

3. 1. Research Background
Background : computer aided diagnosis
• diagnosis assistant system
Classification ( / , )
Segmentation ( , )
Object Detection ( , )
Classification
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4. 1. Research Background
: Multimodal task
https://ai.googleblog.com/2020/04/a-step-towards-protecting-patients-from.html
• Multimodal classification problems
• PR218 - MFAS: Multimodal Fusion Architecture Search
•
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5. 1. Research Background
Introduction
• What & Why
• Teledermatology ( )
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- fourth leading cause of nonfatal disease burden
- affecting 30-70% of individuals and prevalent in all geographies and age groups.
• “store-and-forward teledermatology”
https://www.slideshare.net/Prezi22/powerpoint-4mb
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6. 1. Research Background
Background
• Computer vision deep learning model
• : dermatoscope
• , Dermatoscope
• digital device
• / (Esteva et al. Nature 2017)
• Onychomycosis ( ) (Han et al. PlosOne 2018)
• 198 (Yang et al., 2018 CVPR)
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• , ranked list (differential diagnosis)
Esteva et al. Yang et al.
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7. 1. Research Background
Objective & Approach :
• We developed a deep learning system (DLS) to identify 26 of the most common skin
conditions in adult cases that were referred for teledermatology consultation.
1) 27 class (Differential diagnosis)
2) Image + additional data ( , )
3) 6
4) , ,
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8. 2. Methods

9. 2. Methods
Dataset & model system architecture & training
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10. 2. Methods
Split dataset & data labeling
14 pool (5-30 , 9.1 ) 3
rotation labeling
case , metadata 3
Aggregation class score soft label
Differential diagnosis
Case마다 1~29명의 피부과 전문의 (미국 38명, 인도 5명)이 labeling
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11. 3. Experimental Results

12. 3. Experimental Results
Deep learning system
• validation set accuracy averaged sensitivity
• , Top-3 accuracy sensitivity, AO
DLS: 모델 (deep learning system)
Derm: 피부과 전문의 (dermatologists)
PCP: 주치의 (Primary care physicians)
NP: 임상 간호사 (nurse practitioners)
* Webber et al., A Similarity Measure for Indefinite Rankings, ACM Transactions on Information Systems, 2010
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13. 3. Experimental Results
Subgroup analysis
• subcategory ,
DLS: 모델 (deep learning system)
Derm: 피부과 전문의 (dermatologists)
PCP: 주치의 (Primary care physicians)
NP: 임상 간호사 (nurse practitioners)
Malignant -> 생검 필요
Non-infectious 에서 사용되는 topical steroid 처방이 Infectious 에서는 악화시킬 수 있음
Infectious -> KOH검사 필요
• subcategory
병의 원인이 다르기 때문에 각기 다른 1차 치료가 필요함
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14. 3. Experimental Results
Deep learning system
• Integrated gradients , focus
• hair loss ,
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15. 3. Experimental Results
Importance of input data
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• Clinical metadata self-reported skin problem
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Self-reported skin problem = One of: [Acne | Growth or mole | Hair loss | Hair or nail problem | Hair problem | Nail problem | Pigmentary problem | Rash | Other | Unknown]
plateaued

16. 4. Conclusion

17. 4. Conclusions 17/17
Thank you.
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18. 2. Methods
data labeling