Healthcare Research You
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1 DL for Healthcare
2 Goals Healthcare Research You What are high impact problems in healthcare that deep learning can solve? What does research in AI applications to medical imaging look like? How can you get involved?
3 Goals Healthcare What are high impact problems in healthcare that deep learning can solve?
4 Questions we care about answering in healthcare Descriptive what happened? What was a patient s heart rate through their day?
5 Questions we care about answering in healthcare Descriptive what happened? What was a patient s heart rate through their day? Diagnostic why did it happen? Why is this patient coughing for 2 weeks? Does their chest-xray show signs of pneumonia? Why is this patient palpitating? Does their ECG show signs of atrial fibrillation?
6 Questions we care about answering in healthcare Descriptive what happened? What was a patient s heart rate through their day? Diagnostic why did it happen? Why is this patient coughing for 2 weeks? Does their chest-xray show signs of pneumonia? Why is this patient palpitating? Does their ECG show signs of atrial fibrillation? Predictive what will happen? Will this patient live for the next six months given their medical record? Will this patient develop heart failure as a result of chemotherapy?
7 Questions we care about answering in healthcare Descriptive what happened? What was a patient s heart rate through their day? Diagnostic why did it happen? Why is this patient coughing for 2 weeks? Does their chest-xray show signs of pneumonia? Why is this patient palpitating? Does their ECG show signs of atrial fibrillation? Predictive what will happen? Will this patient live for the next six months given their medical record? Will this patient develop heart failure as a result of chemotherapy? Prescriptive what should we do? Should this diabetic patient be treated with metformin or through lifestyle changes?
8 y a d er t s e Y Descriptive what happened? What was a patient s heart rate through their day? T y a d o Diagnostic why did it happen? Why is this patient coughing for 2 weeks? Does their chest-xray show signs of pneumonia? Why is this patient palpitating? Does their ECG show signs of atrial fibrillation? y a d To Predictive what will happen? Will this patient live for the next six months given their medical record? Will this patient develop heart failure as a result of chemotherapy? Tom w o orr Prescriptive what should we do? Should this diabetic patient be treated with metformin or through lifestyle changes?
9 Stanford ML Group Descriptive what happened? What was a patient s heart rate through their day? Diagnostic why did it happen? Why is this patient coughing for 2 weeks? Does their chest-xray show signs of pneumonia? Why is this patient palpitating? Does their ECG show signs of atrial fibrillation? Predictive what will happen? Will this patient live for the next six months given their medical record? Will this patient develop heart failure as a result of chemotherapy? Prescriptive what should we do? Should this diabetic patient be treated with metformin or through lifestyle changes?
10 What data can we use? Medical Imaging X-ray, CT, MRI, Ultrasound
11 What data can we use? EHR Medications, lab tests, clinical notes Medical Imaging X-ray, CT, MRI, Ultrasound
12 What data can we use? Genomics DNA sequencing, RNA measurements EHR Medications, lab tests, clinical notes Medical Imaging X-ray, CT, MRI, Ultrasound
13 What data can we use? Mobile Smartphones, wearables (ECG, PPG data) Genomics DNA sequencing, RNA measurements EHR Medications, lab tests, clinical notes Medical Imaging X-ray, CT, MRI, Ultrasound
14 Where can decision making be assisted? Clinician radiologist, pathologist, physician Inside a hospital room Medical Imaging
15 Where can decision making be assisted? Clinician radiologist, pathologist, physician Inside a hospital room Medical Imaging Operations Across the hospital EHR
16 Where can decision making be assisted? Clinician radiologist, pathologist, physician Inside a hospital room Medical Imaging Operations Across the hospital EHR Beyond the hospital Mobile Patients
17 How will decision making be affected? Clinician radiologist, pathologist, physician Inside a hospital room Inform, Augment, or Replace Medical Imaging Operations Across the hospital EHR Beyond the hospital Mobile Patients
18 Goals Research What does research in AI applications to medical imaging look like?
19 Deep Learning for Medical Imaging Large Datasets Deep Convolutional Neural Networks Expert Level Performance
20 What s tough? Large Datasets Good labels? How to acquire? Deep CNNs ML process of developing networks Expert Level Performance Evaluating the importance
21 What s been done? Gulshan et al., 2016 Ophthalmology Dermatology Detection of Diabetic Retinopathy Detection of Melanomas Esteva et al., 2017
22 What s been done? Rajpurkar et al., 2017 Gulshan et al., 2016 Ophthalmology Dermatology Cardiology Radiology Detection of Diabetic Retinopathy Detection of Melanomas Detection of Arrhythmias Detection of Pneumonia Esteva et al., 2017 Rajpurkar et al., 2017
23 CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays Pranav Rajpurkar*, Jeremy Irvin*, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng
24 Pneumonia detection is important Infection that inflames the air sacs in lungs. 1 million hospitalizations and 50,000 deaths per year in the US alone. Symptoms: cough with phlegm, fever, chills, trouble breathing. Like people with colds or the flu, but lasts longer.
25 To Diagnose Diagnosis starts with symptoms and a stethoscope. If signs of pneumonia, then take an x-ray.
26 Chest X-ray exam Fast and painless imaging test using x-rays. Usually two views, one from straight on and one from the side of chest. 2 billion chest x-ray procedures per year.
27 Chest X-ray image Ribs and spine will absorb much of the radiation and appear white or light gray on the image. Lung tissue absorbs little radiation and will appear dark on the image. Air appears black.
28 Detecting Abnormalities Abnormalities present mostly as areas of increased density (opacity).
29 X-ray findings of pneumonia Most commonly manifests as consolidation ( fluffy cloud ).
30 X-ray findings of pneumonia Most commonly manifests as consolidation ( fluffy cloud ). Lobar pneumonia: entire lobe consolidated.
31 X-ray findings of pneumonia Most commonly manifests as consolidation ( fluffy cloud ). Lobar pneumonia: entire lobe consolidated.
32 Detecting Pneumonia Pneumonia occurs when alveoli fill up with pus.
33 Confusing Pneumonia Appearance of pneumonia in X-ray images is often vague, and can mimic other abnormalities. If not pus filling up alveoli, but: - Cells (cancer) - Blood (pulmonary hemorrhage)
34 Contributions 1. Radiologist-level pneumonia detection from Chest X-rays. 2. State of the art results on all 14 thoracic pathologies in the largest public x-ray dataset.
35 Setup - Input is a frontal frontal-view chest X-ray image - Output is a binary label t {0, 1} indicating the absence or presence of pneumonia
36 Network Architecture - 2D CNN over 224 x 224 images - Pretrained on ImageNet layer DenseNet Architecture
37 DenseNets - Connect every layer to every other layer in feed forward fashion Densely Connected Convolutional Networks Huang & Liu et al. (2016)
38 DenseNets - Beats previous state of the art (ResNet) and have: - lower error - fewer parameters Densely Connected Convolutional Networks Huang & Liu et al. (2016)
39 Dataset Building off of public x-ray scans
40 Dataset - 112,120 frontal-view X-ray images of 30,805 unique patients - Largest public dataset (released sep 2017) ChestX-ray14 Wang et al. (2017)
41 Dataset - Train Set - Each x-ray annotated with up to 14 different thoracic pathology labels - Annotation by NLP on radiology reports ChestX-ray14 Wang et al. (2017)
42 Dataset - Test Set - We collected a test set of 420 frontal chest X-rays. - 4 Stanford radiologists independently annotated
43 Lots of data & deep network How close to experts can we get?
44 Evaluation -- Metrics Goal: maximize both precision and recall
45 Evaluation -- Assessing Radiologists For each radiologist, we calculate their F1-score using each of the other three radiologists, and CheXNet, as ground truth. ground truth Repeat for test set (420 images) prediction
46 Evaluation -- Assessing Radiologists For each radiologist, we calculate their F1-score using each of the other three radiologists, and CheXNet, as ground truth. ground truth Repeat for test set (420 images) prediction
47 Evaluation -- Assessing Radiologists For each radiologist, we calculate their F1-score using each of the other three radiologists, and CheXNet, as ground truth. prediction ground truth Repeat for test set (420 images)
48 Evaluation -- Assessing Radiologists For each radiologist, we calculate their F1-score using each of the other three radiologists, and CheXNet, as ground truth. prediction ground truth Repeat for test set (420 images)
49 Evaluation -- Assessing CheXNet For our model, we calculate F1-score using the each of the four radiologists as the ground truth. ground truth Repeat for test set (420 images) prediction
50 Evaluation -- Assessing CheXNet For our model, we calculate F1-score using the each of the four radiologists as the ground truth. ground truth Repeat for test set (420 images) prediction
51 Evaluation -- Assessing CheXNet For our model, we calculate F1-score using the each of the four radiologists as the ground truth. prediction ground truth Repeat for test set (420 images)
52 Evaluation -- Assessing CheXNet For our model, we calculate F1-score using the each of the four radiologists as the ground truth. prediction ground truth Repeat for test set (420 images)
53 Evaluation -- Results
54 Evaluation -- Limitations We identify two limitations with our comparison to radiologists: 1. No access to patient history or prior examinations. 2. Only frontal radiographs presented, no lateral views.
55 Evaluation -- Previous Benchmarks Evaluated by AUROC in the binary classification tasks for each of the 14 pathologies.
56 XRay4All With Michael Bereket, Thao Nguyen, and Henrik Marklund
57 Rivaling clinical experts! How do we interpret the algorithm?
58 Model Interpretation Can you trust your model? What parts of an image are most important for diagnosis?
59 Class Activation Maps Learning deep features for discriminative localization Zhou et al. (2016)
60 Pneumonia Multifocal community acquired pneumonia Left lower and right upper lobes
61 Pneumothorax Right-sided pneumothorax
62 Nodules Left lower lung nodule 90% of mistakes in lung cancer diagnosis occurs on chest radiographs
63 AI for pneumonia detection from chest x-rays Can it make an impact?
64 Future of diagnostic access 1. Improve healthcare delivery. CheXNet can help radiologists prioritize workflow and make better diagnoses. 2. Increase access to medical imaging expertise globally. ⅔ of the global population lack access to radiology diagnostics.
65 Goals You How can you get involved?
66 AI for Healthcare Bootcamp with Andrew Ng For ML students intending to get involved in research 2-quarter bootcamp covers a large breadth of topics at the intersection of artificial intelligence and healthcare. Students take a dive into cutting-edge research in AI for healthcare.
67 Next bootcamp in Fall. Applications open today!
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