- Medical Healthcare 3D Imaging AI
- Medical Healthcare AI Application
- Sectorization Growth View
- Medical Healtcare AI Key Solution Blocks
- Deep Dive into Object Detection & Segmentation
- Real-world Medical 3D Imaging AI System Implementation Technology Debt
Medical Healtcare Imaging AI Taxonomy 😃
Medical 3D Imaging | Medical 3D SIMULATIONS (Brain Simulation Platform)|MEDICAL DEVICES (MRI/CT/XRay/PET Scan) |Medical Imaging Stoarge & Exchnage protocol..
Robotic Surgery | Automated Brain Tumour Segmentation | Skin Cancer Lesion Detection & Segmentation (Melonama Recognition) | Lung Cancer detection..
3D IMAGE SEGMENTATION TECHNIQUES | Deep Reinforcement Learning | ROBOTICS..
Here goes the list of some of the widely adopted Real-world AI/ML system Implementations in Medical field
- Radiology
- Skin Cancer Lesion Detection & Segmentation (Melonama Recognition)
- Automated Brain Tumour Segmentation
- Lung Cancer
- Colon Cancer
- Robotic Surgery
- ECG Sleep Apnea Detection
- Sequential Treatment Administering (Sequential Treatment decision using Reinforcement Learning)
- Microsocpy Medical Healthcare (Segmenting Nuclei in Microscopy Images)
P.S.:
- Interesting titbit: AI is better than many dermatologists at diagnosing skin cancer. In a study published in the leading cancer journal - Annals of Oncology
- Dermatologists were only 86.6% accurate at diagnosing skin cancer,
- while the computer was able to diagnose issues with a 95% accuracy. It was also quoted in Fortune magazine published in Y2018.
- Prominent Computer Vision Technique for above applications: Object Detection & Segmentation
Global 3D medical imaging market growth Please refer to the following link for Market-specific growth opportunities in global 3D medical imaging market
Global 3D medical imaging market was valued over USD 6.5 Billion in 2018 and is projected to be worth nearly USD 12.6 Billion expanding at a CAGR of 9.8% from 2019 to 2025.
Medical Healtcare AI Solution is composed of following Key Solution Blocks/ Factors:
- Medical 3D Imaging Overview
- Medical 3D Imaging DEVICES
- Medical 3D Imaging (hardware players) VENDORS
- Medical 3D Imaging SOFTWARES
- Medical Imaging Stoarge & Exchnage protocol / STANDARD (DICOM)
- Medical 3D Imaging DATASET formats
- Medical 3D Imaging DATASET Handling/ PRE-PROCESSING PYTHON Libraries
- Data Annotation Techniques
- 3D IMAGE SEGMENTATION Computer Vision Techniques & Evolution
- Sequential Treatment Administering (Reinforcement Learning)
- AI/ML Model Productionaization/Industrialization (Hiddent Technical Debt in ML System)
Prominent Computer Vision Technique for above applications is Object Detection & Segmentation Computer Vision Technique. Let us deep dive into the Object Detection & Segmentation Computer Vision Technique.
# HIDDEN TECH DEBT IN ML SYSTEM- NIPS/GOOGLE 2015
# Only a SMALL FRACTION of REAL-WORLD ML SYSTEMs is composed of the ML CODE
_Courtsey Google NIPS 2015
[HIDDEN TECH DEBT IN ML SYSTEM- NIPS/GOOGLE 2015](https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf)
# Hardest part of AI isn't AI, but it's Data & productionization
# Productionization/Industralization Machine Learning Systems
MLOPS IS THE SILVER BULLET!
Please refer to Enterprise AI MLOPS for deeper recepi in MLOPS.
AI/ML Model Productionaization/Industrialization (Hiddent Technical Debt in ML System)
- MLOps - Iterative Experimentation (Kubeflow)
- Model Enrichment - Transfer Learning & HPO - AutoML (AutoKeras)
- Scalable & Secured "AI-as-a-Service"
- AI Serving Library (TF-Serving, ONNX Runtime, Seldon Core)
- RESTful API/ gRPC API
- Microservice Management (API Gateway, Service Discovery, Service Registry, Service Config etc)
- Service Mesh (Istio)
- Containerization (Docker) & CaaS (K8S) -
- AI Inference Service Routing
- Canary Deployment
- Blue Green Deployment
- Multi Armed Bandit Deployment
- A/B
- AI Trust
- Feature Bank (Feature Set)
- Model Interpertability (LIME, DeepShap)
- AI Continous Monitoring System (AWS Sagemaker Monitor)
- Data Drift
- Concept Drift
- Serverless (KNative, OpenFaaS/ Apache OpenWhisk)
- Edge Deployment, such as on Mobile (TFX - TensorRT, TFLite | EdgeX)
- Hybrid Processing (Local Edge Processing + Centralized Cloud Processing)
- Mobile & Web App (PWA)
Feel free to contact me to discuss any issues, questions, or comments.
My contact info can be found on my GitHub page.
I, The DeepHiveMind, am providing code and resources in this repository to you under custom Copyright & license (Copyright 2019 DeepHiveMind & Creative Commons Legal Code CC0 1.0 Universal). Please Refer to the [Copyright 2019 DeepHiveMind License] for further details as to this. Thanks!