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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