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Identifying Medical Diagnoses and Treatable Diseases by Image-Based
Deep LearningKermany et al., 2018, Cell 172, 1122–1131
February 22, 2018 ª2018 Elsevier Inc.
Group 04: 簡瑞霖、黃健祐、黃崧瑋
2018/04/19
https://doi.org/10.1016/j.cell.2018.02.010
Key Concepts
biomedical imaging interpretation and medical decision making
• clinical-decision support algorithms
• A.I.
• transfer learning techniques
• reliability and interpretability by occlusion test
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Terminology
Age-related Macular Degeneration (AMD) 老年性黃斑部病變
Choroidal Neovascularization (CNV) 眼球脈絡膜血管增生
Diabetic macular edema (DME) 糖尿病黃斑部水腫
Multiple drusen 隱結
• 老化黃斑部細胞代謝下降使得物質累積產生沉積
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Introduction
Image-based deep learning classifiers on • age-related macular degeneration(AMD) and diabetic
retinopathy on retinal optical coherence tomography (OCT) images
• bacterial and viral pneumonia on chest X-rays
Technique: transfer learning techniques
Goal :
• Expediting the diagnosis and referral
• Facilitating earlier treatment
Improved clinical outcomes
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Optical Coherence Tomography(OCT)
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• non-invasive imaging test
• see each distinctive layers
map and measure their thickness
Key Pathology in each image
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Traditional algorithmic approach
(1) handcrafted object segmentation
(2) identification of each segmented object using statistical classifiers or shallow neural computational machine-learning classifiers designed specifically for each class of objects
(3) classification of the image.
Creating and refining multiple classifiers required many skilled people and much time and was computationally expensive
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Convolutional neural network(CNN) layers
Image analysis filters, or convolutions, are applied.
Feature map
• 圖片裡的各個局部,這些局部被稱為特徵(feature)
The image-to-classification approach in one classifier replaces the multiple steps of previous image analysis methods.
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Transfer Learning
lack of data in a given domain leverage data from a similar domain (TL)
training a completely blank network
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locally connected
Results
“urgent referrals”: demand relatively urgent referral to an ophthalmologist for definitive anti-VEGF treatment
“routine referrals”: images with drusen, which are lipid deposits present in the dry form of macular degeneration
“observation” : Normal images
Highlighting the regions recognized by the neural network
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“Limited model classifiers” Multi-class model , Binary Model…
Plot showing Performance in Training and validating datasets using Tensorboard
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Training
Validating
Multiple class comparison between CNV,DME Drusen and normal
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CNV,DME
Confusion table
Weighed error based on penalties
Diagnostic performance-
Model v.s Experts
statistically similar(CI:
95%)
99.9%
Limited model:
aROC ~= 95%
Normal model:
aROC ~= 97.5%
Occlusion Testing
Drusen: located correctly (100%)
CNV accuracy : 94.0%
DME accuracy : 91.0%
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Diagnosis of Pediatric Pneumonia
• single leading cause of childhood mortality
** developing countries: rapid radiologic interpretation of images is not always available
A. Bacterial pneumonia requires urgent referral for immediate antibiotic treatment
B. viral pneumonia treated with supportive care.
• accurate and timely diagnosis is imperative
• radiographic data (X-ray)4/23/2018
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Pneumonia versus Normal Bacterial v.s. viral pneumonia
96.8% 94%
Performance of Pneumonia Diagnosis using X-Ray Images
Discussion
• Occlusion test reveals insights into the decisions of neural networks.
Transparent and interpretable diagnosis
Transfer Learning:
• Database: (10k) vs (1k) images?
• Performance of model highly depends on weights of pre-trained model
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Summary
Image-Based Deep Learning facilitate screening programs and create more efficient referral systems in all of medicine, particularly in remote or low-resource areas, leading to a broad clinical and public health impact.
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