Resource of free step by step video how to guides to get you started with machine learning.
Thursday, June 24, 2021
Building AI models for healthcare (ML Tech Talks)
In this session of Machine Learning Tech Talks, Product Manager Lily Peng will discuss the three common myths in building AI models for healthcare. Chapters: 0:00 - Introduction 1:48 - Myth #1: More data is all you need for a better model 6:58 - Myth #2: An accurate model is all you need for a useful product 9:15 - Myth #3: A good product is sufficient for clinical impact 12:19 - Conversation with Kira Whitehouse, Software Engineer 34:48 - Conversation with Scott McKinney, Software Engineer Resources: Deep Learning for Detection of Diabetic Eye Disease: Gulshan et al, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016 → https://goo.gle/3gVhTxs A major milestone for the treatment of eye disease De Fauw et al, Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine September 2018 → https://goo.gle/35Sfs9C Assessing Cardiovascular Risk Factors with Computer Vision. Poplin et al, Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. March 2018 → https://goo.gle/3qkg01I Improving the Effectiveness of Diabetic Retinopathy Models: Krause et al, Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology August 2018 → https://goo.gle/3gR8d8n Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. Raumviboonsuk et al. NPJ Digital Medicine. April 2019 → https://goo.gle/2SmyXUO Healthcare AI systems that put people at the center: Beede et al, A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. CHI '20 April 2020 → https://goo.gle/3ja6TyP Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. MScPH, Yuchen Xie, Quang D. Nguyen BEng, Haslina Hamzah BSc, Gilbert Lim, Valentina Bellemo MSc, Dinesh V. Gunasekeran MBBS, Michelle Y. Yip, et al. The Lancet → https://goo.gle/3zVec3q Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow
Labels:
Video
Published by Free artificial intelligence and machine learning video tutorial resource
Related video tutorials
Subscribe to:
Post Comments (Atom)
Most watched
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Quick answer: For machine-learning work, buy an external SSD over a spinning hard drive, pick USB 3.2 Gen 2 (10 Gbps) or faster, and size f...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
Quick answer: Yes, for most budget builders: a used RTX 3090 is still the cheapest route to 24 GB of VRAM with full CUDA support. Capacity...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: The cheapest path to serious local-AI VRAM is not one new flagship card — it is two used ones. A single RTX 5090 costs $1,999...
-
Quick answer: FLUX.1 dev needs about 10 GB of VRAM at Q4/NF4 quantization for 1024×1024 image generation, so a 12 GB GPU such as the GeFor...
No comments:
Post a Comment