지난주말에 있었던 제 4회 대한신경집중치료학회 편집위원회 워크샵에서 발표했던 내용중에 발췌한 것입니다. 원래 제목은 "인공지능 관련 연구: 논문 작성과 심사에 관한 요령" 입니다. 최근에 deep learning in medical imaging으로 2편의 리뷰와 논문 1편, CADD 논문, 앙상블 논문 1편이 되면서 요청이 온것 같습니다.부족한 제가 하기 어려운 주제를 맡았는데, 혹시 도움이 되실 분이 있으면 도움을 되시라고 올려드립니다. 결론은 인공지능 연구라고 특별히 다르지는 않지만, 공학 연구와 의학연구가 다르고, 인공지능 특성을 잘 이해해야 한다 정도 될것 같습니다. (상당부분 저희병원 박성호 교수님의 radiology 논문 Methodology for Evaluation of Clinical Performance and Impact of Artificial Intelligence Technology for Medical Diagnosis and Prediction을 참고했습니다.)
OpManager is an integrated network management tool that helps you monitor your network, physical & virtual servers, bandwidth, configurations, firewall, switch ports and IP addresses
OpManager is an integrated network management tool that helps you monitor your network, physical & virtual servers, bandwidth, configurations, firewall, switch ports and IP addresses
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
En utilisant l’apprentissage de models sur des données collectées dans les dossiers patients d’un réseau d’hôpitaux et du machine learning, il est possible de prédire le risque de ré-hospitalisation dans 30 ou 90 jours pour des insuffisants cardiaque. Valère présente la création d’un Cloud Collaboratif sur le Cancer qui offre la possibilité aux Hôpitaux des Etats Unis de donner accès à un très grand nombre de dossiers patients atteint du Cancer.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
En utilisant l’apprentissage de models sur des données collectées dans les dossiers patients d’un réseau d’hôpitaux et du machine learning, il est possible de prédire le risque de ré-hospitalisation dans 30 ou 90 jours pour des insuffisants cardiaque. Valère présente la création d’un Cloud Collaboratif sur le Cancer qui offre la possibilité aux Hôpitaux des Etats Unis de donner accès à un très grand nombre de dossiers patients atteint du Cancer.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nucleic Acid-its structural and functional complexity.
인공지능 논문작성과 심사에관한요령
1. 인공지능 논문작성과 심사에 관
한 요령
1
Namkug Kim, PhD
namkugkim@gmail.com
Medical Imaging & Intelligent Reality Lab
Convergence Medicine/Radiology,
University of Ulsan College of Medicine/Asan Medical Center
South Korea
3. Big data : Google
Trends/Facebook
4
Nature 2008
약물중독의 상관관계를 분석하여 담배, 알코올, 의약품의 중독에 대해 각각 86%,
81%, 84%의 정확도로 선별
4. Big data : IoT Thermometer
• IoT 스마트폰 체온계
– 미국의 헬스케어 스타트업
Kinsa :
– 미국 전역에서 실시간으로 측정
되는 '체온 빅데이터’
– Patients-derived health data
– 아이들의 학교 기반으로도 데이
터
• CDC 독감 통계 몇주 vs Kinsa
실시간
• B2B 모델:
– 수요나 생산량
• 독감 예방 접종 혹은 항생제, 살균제
등의 약
• 칫솔, 오렌지 쥬스, 수프 등
5
독감의 트렌드: 2.5년 동안 CDC의 결과 비교
5. Opportunity
6
8 trillion exam:
Healthcare
Industry
2 trillion : wastes in
healthcare industry
Better
experience
Imaging :
Unnecessary tests
Lower cost
Oncology:
Variability of Care
Better outcomes
Life sciences:
Failed clinical trials
Government:
Fraud, Waste and
Abuse
Value Based Care:
Cost of chronic
disease
360 billion : total IT
and healthcare
market opportunity
*IBM Watson
6. Artificial Intelligence(인공지능)
7
Machines (SW, robots) that think and act like humans
Make machines do things at which humans are better
Solve tasks that, if done by humans, require intelligence
1950: Turing’s paper, 1956: “Artificial Intelligence (AI)”
8. New Definition
• Any device that perceives its
environment (E) and takes actions (A)
that maximize its chance of success
at some goal (G).
• a machine mimics "cognitive" functions
that humans associate with
other human minds, such as "learning"
and "problem solving”
9
9. Artificial Intelligence(인공지능)
• Weak artificial intelligence(약인공지
능)
– Narrow AI, applied AI
– 정해진 목적을 위해 사용하는 인공지능
• 바둑, 체스, 스팸 필터링, 쇼핑 추천, 자율운전
• Strong artificial intelligence(강인공
지능)
– 인간급의 인공 지능
– 사고, 계획, 문제해결, 추상화, 복잡한 개념
학습
10. 지도학습 vs 비지도학습
• Supervised Learning (지도학습)
– Naïve Bayesian Classifier
– Support Vector Machine
– Artificial Neural Network
• Deep Learning
• Unsupervised Learning (비지도학습)
– k-means
13. Deep learning & Medicine
• Keyword Search “Deep learning” in PubMed
Updated on September 14th, 2017
14. Better Decision in Medicine:
Clinical Decision Support System /
Risk Prediction
• Precision medicine
– Massive search of medical information
– Mining medical records
– Advanced analytics
– Designing individualized treatment plans
• Individualized/group risk prediction
16. Better Patient Management
• Health assistance and medication
management
• Getting the most out of in-person and
online consultations
• Open AI helping people make
healthier choices and decisions
17. Medication Monitoring
Solution▪ AiCure
▪ A provider of a facial recognition and
motion sensing technology to medical
ingestion
-Substantial funding from pharmaceutical
industry, academic collaborators, and the
National Institutes of Health
▪ Combine machine learning with
smartphone technology to remind
people to take their medicine
▪ The data it provides to its systems
transmits in real time back to a clinician
through a HIPPA – compliant network
-Clinicians conforming through the system that
the patients are taking their medicine as
instructed
Sends the patient
a reminder, and
then requests that
they use the
camera built into
their phone to
video themselves
taking the
medicine
Visually confirms that
the person in the video
is the patient, and then
to identify the pill in
the mouth of the
patient to prove that
they have taken their
medicine
1) Since 2009, New York-based, $12M Funding
19. Drug Discovery
http://fortune.com/2016/04/22/berg-pacreatic-cancer-artificial-intelligence/
http://tech.co/berg-medicine-artificial-intelligence-2016-07
http://www.wired.co.uk/article/niven-r-narain-ai-drugs-wired2015
• Data analytics software + in-
the-lab drug development to
find new treatments
• Analysis of massive amounts
of biological data to
uncover unexpected
connections between healthy
and sick patients
-The resulting insights allow for a
more informed hypothesis, which in
turn enables more efficient drug
development
-Provides real time analytic
solutions that predict the impact
of treatment plans at the
individual level to optimize
population health strategies
✓ Starts by drawing sequencing data from human tissue
samples, as well as information about protein formation,
metabolites, and other elements of functional data.
✓ The process produces trillions of data points from a single
sample. The data is then combined with patient clinical
information and analyzed by our proprietary artificial
intelligence machine learning analytics program.
The BERG Interrogative Biology® Platform
20. AI Application in Medical
Imaging
• Almost all aspects
– Image transformation
– Lesion segmentation
– Lesion classification
– Lesion detection
– Finding similar cases
– Assistance of interpretation
21.
22. • Skin lesion using images
– 129,450 images
– 757 classes / 2032 diseases
• Validation
– Benign vs. Malignant vs. non-neoplastic
– Nine classes with similar treatment plan
• Test
– Malignancy vs. Benign
* Would be applicable to smartphone: universal access to vital diagnostic care
23. 62
• 5만장의 사진을 학습
• region-based convolutional
neural network (R-CNN)
42명의 피부과 전문의와 진단능력
비교
• 한승석, 장성은 (AMC)
24. Computer Vision and Pattern
Recognition
Mar. 2017
• Task: Detection of LN metastasis from breast ca.
• Data: Camelyon 16 dataset
• Network: Inception V3
• Results:
– 92.4% sensitivity (with 8 FP per image)
– Cf. 82.7% human pathologist
25.
26. Surgical Robot with AI
• Will robot steal surgeons’ job?
– NO.
• Will robot CHANGE surgeons’ job?
– It may...
• Will robot and SUPER COMPUTER steal surgeons’ job?
– …
28. 인공지능 의료적용 분야
인공지능 분야
시각지능
언어지능
판단지능
자동분류
요약/창작
공간지능
임상시험
케이스선정
신약개발프로세스
진료보조
비서서비스
음성인식 의무기록
데이터기반 정밀의료
유전체분석
약혼합사용 및 합병증 예측
진단검사추천
판독보조
정상유무판정
유사증례검색
판독문 생성
병리분야 판독 보조
물류, 수술실, 병실 운영
로봇수술
의료 인공지능
인공지능 의료적용 분야
68
29. Artificial Intelligence (+Big Data) Will
Redesign Healthcare
1. Precision medicine / Mining medical records /
Designing treatment plans
2. Getting the most out of in-person and
online consultations / Health assistance and
medication management / Open AI helping
people make healthier choices and decisions
3. Assisting repetitive jobs
4. Drug discovery / Clinical trial Case Matching
5. Analyzing, redesigning a healthcare system
http://medicalfuturist.com/ Aug, 2016
36. Human Segmentation
Human Segmentation
AI_ 1st Test
AI_ 1st Test
AI_ 2st Test
AI_ 2st Test
AI_ 3rd Test
AI_ 3rd Test
rebuilding ground truth increasing data
0.88
Active Learning
DICE 0.91 0.95
37. 2. 음성/시그널 인식 및 번역
Speech
Recognition
Machine
Translation
Speech Recognition + Machine
Translation
40. 3. 비디오 인식
Video understanding (Google, 2014)
Scene parsing (NYU/Facebook , 2014) NVIDIA DRIVE PX, 2015
Google Lens 4 General Sensor
41. 중이염 동영상 인식 및 모니터링
AMC ENT 정종우 교수님 협력연구
수술장 동영상 기술 개발
중이염, 인공와우 수술 동영상자료의 분류
수술영상을 각 단계로 분류 : 1000 례 이상
분류된 영상의 구조물 명시와 시술 단계 구분 및 예측
93
수술장 영상 기술 개발
영상 내 물체 인식의 경우
동영상 같은 경우에는 CNN과 RNN을 결합
한 CNN-RNN 모델 사용
동영상 데이터 학습을 통한 수술 단
계 분류 알고리즘
- 개발정리된 영상을 이용한 인공지능
학습 알고리즘 개발
- 좌우측과 다양한 형태의 유양동에 따
른 적용
- 알고리즘의 임상 검증 및 평가(수술단
계 및 정량화 정보)
AMC ENT 정종우 교수
43. Deep Learning for Endoscopy:
Detection and Classification of Colonic Polyp
▪ Overall architecture
Polyp detection
in endoscopy (video)
Close-up shot on
polyp of interests
Pathological
Diagnosis
(multi-modal)
Benign Adenoma
(0.98)
• https://arxiv.org/abs/1512.03385 “Deep Residual Learning for Image Recognition”
• https://arxiv.org/abs/1512.04150 “Learning Deep Features for Discriminative Localization”
44. DDx: NICE Classification of Colon
Polyp
Data
NICE I: 25
NICE II: 89
NICE III: 19
Byun JS, Park B, Kim N, AMC
45. 5. GAN
Deep Convolutional Generative Adversarial Networks (DCGAN)
Rotations are linear in latent space
Bedroom generation
Arithmetic on faces
47. 6. 영상 해석/자막 생성
Image Caption
Generation
Video Caption
Generation
48. Generated annotation
- Hoo-Chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M Summers, "Learning to Read Chest X-Rays:
Recurrent Neural Cascade Model for Automated Image Annotation," CVPR 2016.
50. 105
The Previous Research
for Quantification Definition of Similar Lung Images
Extraction of Distribution Features Extraction of Distribution Features
* https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor
* Y.J.Chang, et al,. “A support vector machine classifier reduces interscanner variation in the HRCT classification of regional
disease pattern in diffuse lung disease: Comparison to a Baysian classifier”, Medical Physics 40 (5), 051912 (2013)
AMC 영상의학과 서준범, 이상민 교수
53. RSNA 2017
Theme : “Explore, Invent, Transform”
About 70 scientific sessions related with
ML/DL
– Not radiology conference, but AI conference
Machine Learning Pavilion (Showcases)
45 Companies
Machine learning theater
Educations
Machine Learning
Nvidia DLI handson
108
54. Interpretability : Machine Operable, Human Readable
Visual attention
Category – feature mapping
Sparsity and diversity
109
55. Uncertainty
Uncertainty of training data
In clinical situation, it is common
Deep Bayesian Modeling
Uncertainty of classification/prediction of
Machine Learning
115
56. Novelty (Untrained catergory)
In clinical situation
Novelty is everywhere, especially supervised
learning
Rare diseases, but well known to medical doctors
Hard to training
How to determine novel (untrained) category
Unsupervised learning
Semi-unsupervised learning
Normal vs abnormal
Abnormality Detection
116
58. Big data PACS platform
Bigdata PACS
Arterys, Zebra Medical
Vision
Quantifying every data
Transforming PACS into big
data platform
Advanced processing service
(APS)
Lung nodules
– Detection : location
– Segmentation : boundary
drawing 119
60. AI apps Platform
의학의 발달은 terminology의
발달
Contemporarily contradictory,
and evolutionary
AI apps platform
Philips, Nuance, Siemens, etc
영상의학과 의사의 tasks : 약 5
만개
새로운 질환 발견 정의
의료영상 장비의 발달
121
64. Radiomics 기반기술
영상분할
157
영상 정합 영상 분할 정량 특징 획득 분석 (자동분류자)
영상처리를 통한 Radiomics* * Modified from Nature Comm. 5, #4006
영상정합 다차원분석, 검색기능영상영상표준화 영상시스템
• 다기관/이종장
비 표준화영상
획득
• 영상저장 표준
화 (DICOM)
• 다기관지원
• 클라우드 PACS
• 인포매틱스시
스템 통합분석
• 실감형 정보가
시화 (모바일)
• 다양한 영상정
보 통합
• 이종임상영상
간 정합 (MRI,
PET, CT, 병리
등)
• 병소 구분
• 크기, 형태분석
• 질환별, 영상별
최적 분할 기술
• 고속처리, 시각
화기술
• 관류, 확산능
등 다양한 기능
정량분석
• 질환별, 영상별
최적 모델링기
술
• 다중모달리티 정보통합
및 다차원분석 인공지능
• 다차원영상 시각화
• 영상간 유사도 검색
• 이미지 온톨로지 검색
Radiomics
65. Technical Issues
질환별 빅데이타 시스템 구축시
시간 소요 및 질 (외적 타당도 검증)
• 기구축 고품질 코호트 이용
• 전담 전문인력 배치를 통해 질 보증
• 지속적 다기관 영상 데이타 축적
의료기관마다 상이한
영상 및 의료 데이타
• 영상프로토콜 표준화, 팬텀 개발
• 정량 분석의 자동화를 통해 재현성
• 의료 공통데이타모델 (CDM) 사용
158
질환이 심한 영상 자동 분할 실패
/ 2D 영상기반 영상처리기술
• Multi-atlas 기반의 영상분할 기법 이용
• 기개발기술 환자특징요소 보완/고도화
• 3차원 질감 및 형상 영상요소를 추출
임상환자의 질환 등의 변이
/ 100만건 이상의 대용량 영상 처리
/ 모바일환경 대응
• Self feature 생성 기법
• Deep Learning 등 최신 기법 도입
• GPU CUDA, OpenCL, OpenMP등 병렬화
• 모바일 환경 인터페이스 구축
빅데이타 공개에 따른
개인정보 침해
• 데이타 익명화 경험활용
• Virtual DB 형태의 개별데이타 이용
68. Imaging in Drug Development
beyond diagnostic imaging
• Biomarker = Biological marker
Objective indicator of a biological, pathological or pathogenic
process
• Quantitative
• Accuracy & Precision (Producibility)
• Multi-center
Robust acquisition protocol
• Multiple scanner platforms
• Acquisition protocol by analyst/reader + physicist
• Same-scanner imaging
Scanner qualification & quality control
• Phantoms
Site personnel engagement
• Training & education
• Radiologist as co-PI?
waterarc
Outer air1
Inner air
Bed 1 Bed 2
Outer air2
CT Phantom
Manufact
urer
Scanner kVp Tube cur
rent
(mA)
Average e
ffective tu
be current
(mA)
Slice thick
ness
Pitch Gantry r
otation ti
me
Reconstru
ction Filter
Siemens Sensation 16 140 200 100 0.7 1.000 0.5 B30
Siemens Sensation 64 140 270 99.9 0.7 1.000 0.37 B30
GE LightSpeed 16 140 190 101.2 0.625 0.938 0.5 Standard
GE LightSpeed VCT
64
140 250 101.6 0.625 0.984 0.4 Standard
Philips Brilliance 16 140 142 100.2 0.8 1.063 0.75 B
Philips Brilliance 64 140 135 99.9 0.625 1.014 0.75 B
Variation in Emphysema indexes (%) from four
different CT scanners.
Before DC After DC
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5
Time Point(s)
EmphysemaIndex(%)
Density Correction (outair) ; Standard ; -950 HU Thresholding
Siemens 16
Philips 16 (2)
Philips 40
Toshiba 64
Outsider air volume density correction based on
water and air in four different CT scanners
(Water was assumed as 0 HU and air as -1000 HU).
FEV1 FEV1/FVC
Emphysema index
(Base)
-0.318
0.002
-0.510
<0.001
Emphysema index
(Inner air correction)
-0.597
<0.001
-0.612
<0.001
Emphysema index
(Outer air correction)
-0.394
<0.001
-0.497
<0.001
Mean lung density
(Base)
0.259
0.011
0.460
<0.001
Mean lung density
(Inner air correction)
0.487
<0.001
0.528
<0.001
Mean lung density
(Outer air correction)
0.383
<0.001
0.499
<0.001
. Partial correlation analysis adjusted by age and
sex between CT and PFT parameters in Philips
and Toshiba (n=98).
Accepted at RSNA 2011
69. Issues
• High-dimensional or over-
parameterized diagnostic/predictive
models using artificial deep neural
network
• statistical methods
– assessing the discrimination and
calibration performances
• effects of disease manifestation
spectrum and disease prevalence on
the performance results.
164
70. Data Set
• The performance using internal and
external datasets
• importance of using an adequate
external dataset obtained from a well-
defined clinical cohort
– to avoid overestimating the clinical
performance due to overfitting in high-
dimensional or over-parameterized
classification model and spectrum bias,
and the essentials for achieving a more
robust clinical evaluation.
165
71. External Validation
• data obtained in newly recruited patients
(referred to as temporal validation)
• collected by independent investigators at a
different site (referred to as geographic
validation)
• randomly split from the entire dataset and
kept untouched for use as a test
– (as in the training-validation-test steps) dataset,
while the remaining main portion of the data is
used for the training and the validation steps
166
72. Effect of Spectrum On
Diagnostic/Predictive Performance
• Prospective clinical trials,
– which typically recruit subjects uniformly and
consecutively according to eligibility criteria
explicitly defined for a particular clinical setting,
• Data for a deep learning
– for collected from multiple heterogeneous
sources
– unnatural ratio between disease vs normal
• e.g., severity, stage, or duration
– disease;
– presence and severity of comorbidities;
– demographic characteristics
167
73. Effect of Prevalence On Prediction
of Probability
• The probability of disease
– when a particular test result is given,
• as the post-test probability,
– determined by the LR of the test and pretest
probability
• (i.e., disease prevalence) according to Bayes’ theorem
• pretest odds × LR = post-test odds,
– where odds = probability / (1 − probability)
• post-test probability = pretest probability × LR ÷ (1 −
pretest probability + pretest probability × LR) = prevalence
× LR ÷ (1 − prevalence + prevalence × LR).
168
74. What’s Consideration
• Clinical trials and observational outcome
studies
– for ultimate clinical verification of
diagnostic/predictive artificial intelligence
tools
– through patient outcomes, beyond
performance metrics, and how to design
such studies.
169
83. ROC Analysis
ROC curve of a hypothetical machine learning algorithm that aims to distinguish
lung cancer and benign lung nodules obtained from the results in Table. The AUC
value is 0.923.
84. Calibration
Example calibration plot. The x-axis shows average predicted probability values for each decile,
and the y-axis show the corresponding observed probability in each decile. The error bars represent
95% CIs of the mean predicted probabilities.
87. Clinical Trial Design for AI (II)
Clinical trial designs to assess the impact of an artificial intelligence tool on patient outcome.
B. Cluster randomization of time periods. (E.g, random sequence of four time periods)