Contag: Able to combine fields of view - camera, wide-field fluorescence, microscope with multispectrum #AACR17
7:21am April 3rd 2017 via Hootsuite
Contag: Targeting accessible organs, hollow organs, and surgically-accessed organs. Shows video - find bright stained spot... #AACR17
7:20am April 3rd 2017 via Hootsuite
Contag: Custom manipulator controls; optically able to do multispectral microscopy, a micromachined scanning device 1.5mm #AACR17
7:19am April 3rd 2017 via Hootsuite
Contag: Shows endoscope device, w/both macroscopic and microscopic field of view. Advances in optical SW enables stitching of images #AACR17
7:18am April 3rd 2017 via Hootsuite
Chris Contag (Stanford CA) The future of cancer imaging #AACR17
7:17am April 3rd 2017 via Hootsuite
@JMill_tweets @shenhui1986 Thanks @shenhui1986 for the laugh!
5:17am April 3rd 2017 via Hootsuite in reply to JMill_tweets
RT @JMill_tweets: April fools joke in presentation, nice #AACR17 https://t.co/J51KMNG5Rb
10:35pm April 2nd 2017 via Hootsuite
MT @robertmarc60: Many have wrecked on the shoals of neurosci https://t.co/epuFV6OUG1 Sld 4 https://t.co/bxEG8SSeqn https://t.co/lwbjlBIro5
9:40pm April 2nd 2017 via Hootsuite
dbGaP asks for users' feedback | Repositive https://t.co/RmF3YL83Z8
8:50pm April 2nd 2017 via Hootsuite
RT @ "Collectively, rare diseases are not rare. #raredisease https://t.co/MdCZUmxigv"
7:45pm April 2nd 2017 via Hootsuite
DePristo: 54 doctors, '16 JAMA https://t.co/gQZlkrZytk F-score 0.95 human 0.91. 'Lives up to the hype' commentary #AACR17
2:01pm April 2nd 2017 via Hootsuite
DePristo: Challenge - ophthalmologist graders (on a 5-point scale from none to proliverative), and showing consistency not great #AACR17
2:00pm April 2nd 2017 via Hootsuite
DePristo: Onto diabetes and retinopathy - healthy vs diseased images of the retinal fundus. Looking for hemorrhages. #AACR17
1:59pm April 2nd 2017 via Hootsuite
DePristo: Shows human error rate is higher (around 5%) cp to computers in '15 (below the human error rate) ImageNet Challenge #AACR17
1:56pm April 2nd 2017 via Hootsuite
DePristo: Cat vs Dog: have been working on it for >4y. Shows performance of errors from '10 at 25% error; 2012 15%; '14 7% #AACR17
1:55pm April 2nd 2017 via Hootsuite
DePristo: Layer and layer of matrices, of different dimensions. Ray-loop multiplies and adds; shows basket of animals - cat or dog? #AACR17
1:54pm April 2nd 2017 via Hootsuite
DePristo: Recognizing a cat: learning features from raw, heterogeneous data; no explicit feature engineering req'd #AACR17
1:53pm April 2nd 2017 via Hootsuite
DePristo: Organized in layers, working together to solve complex tasks. Layered network architecture, new training math, scale #AACR17
1:52pm April 2nd 2017 via Hootsuite
DePristo: Deep learning revolution: modern reincarnation of artificial neural networks. Collection of simple trainable math units #AACR17
1:51pm April 2nd 2017 via Hootsuite
DePristo: Trad machine learning - filtering spam via giant rule-based system; new way: build filters that reduce errors and improve #AACR17
1:50pm April 2nd 2017 via Hootsuite
DePristo: AI=making computers smarter; machine learning-making computers that learn #AACR17
Mark DePristo (Google) Deep learning in medicine - an intro and applications to NGS and disease diagnostics #AACR17
1:49pm April 2nd 2017 via Hootsuite
Cohen: Invites others to participate in a Principal Investigators meeting next week in Arlington VA #AACR17
Cohen: From language in paper, to representation, to PySB (Python System Biology) to model to simulations #AACR17
1:47pm April 2nd 2017 via Hootsuite
RT @itsthebunk: .@AACR Summary re #ProjectGenie #AACR17 https://t.co/zhC6XMmpor
1:44pm April 2nd 2017 via Hootsuite
Cohen: Shows example for drug repurposing, from groups in Chicago, Manchester. Another example for Vemurafenib resistence #AACR17
1:43pm April 2nd 2017 via Hootsuite
RT @Nikhilwagle: Proud to stand with 20,000 cancer researchers at #AACR17 in support of sustained @NIH funding for cancer research @AACR ht…
1:41pm April 2nd 2017 via Twitter Web Client
Cohen: 3 curators, of 10 papers, had 34 interactions across all 3. Machines - found 17/34. Sensitivity 50%; lots of FPs. #AACR17
1:39pm April 2nd 2017 via Hootsuite
Cohen: But that info is distributed all across the paper. Human curators are the ground truth (via MITRE) 10 papers example #AACR17
1:38pm April 2nd 2017 via Hootsuite
Cohen: Reading 'has never been easy'; NLP has faced many challenges. Shows a single paper - wants MAPK1-P phosphorylates SMAD2 #AACR17
1:37pm April 2nd 2017 via Hootsuite
Cohen: Machines read journal articles, 300K at 5s/paper. Assembled, multiple models are managed, then used for testing, reasoning #AACR17
Cohen: Underlying slide available from NCI website here https://t.co/LzUrHxJkjq 22K interaction w/Ras pathway #AACR17
1:35pm April 2nd 2017 via Hootsuite
Cohen: Eventually get to causal understand in complicated systems, where currently correlation exists. Started with Ras, McCormick #AACR17
1:34pm April 2nd 2017 via Hootsuite
Paul Cohen (DARPA) Big mechanism - how machines read the cancer literature and build cell-signalling models #AACR17
1:33pm April 2nd 2017 via Hootsuite
Chin: Training requires right kind of data - real world data is messy and noisy. Yet critical the right kind of training data avail #AACR17
Chin: AI systems are taught, not programmed, by examples of 'right decisions'; ground truth needs to be true. #AACR17
1:32pm April 2nd 2017 via Hootsuite
Chin: AI systems are human/machine hybrids; designed for assisting human decisions; algorithms are tools. #AACR17
1:31pm April 2nd 2017 via Hootsuite
Chin: 3rd challenge transparency. 4th: workforce, who has ability to develop a system that makes sense. 'Not a job for the SW devs' #AACR17
1:30pm April 2nd 2017 via Hootsuite
Chin: EHR, kiosks, devices/wearables. Challenges: training sets. Ground truth 'is very hard - very rare black/white in medicine' #AACR17
1:29pm April 2nd 2017 via Hootsuite
Chin: Building data from clinical setting, real-world data (home, retail sites, 3rd party, pt-generated wearables) #AACR17
Chin: Who to get data into AI system? Understanding the Patient as a person, not just a diagnosis. #AACR17
1:28pm April 2nd 2017 via Hootsuite
Chin: New feature requests were 3%; clinicians asking for something already asked for. Adverse event tracking, and cohort them #AACR17
1:27pm April 2nd 2017 via Hootsuite
Chin: A failure of humans to update the data; 24$ derived data - redundant negation w/in 'exclusion criteria' double-negative use #AACR17
1:26pm April 2nd 2017 via Hootsuite
Chin: Root cause analysis; 47% issues were UI/UX; 23% were conflicting Dx in documentation (conflicting 'definitive diagnosis') #AACR17
1:25pm April 2nd 2017 via Hootsuite
Chin:Training the user, quality improvement/safety; 40 users and feedback from leukemia study. Summaries useful; Rx rec's useful #AACR17
1:24pm April 2nd 2017 via Hootsuite
Chin: (For those watching at home - WSJ backstory a few weeks ago in WSJ https://t.co/WopphGQ1BW #AACR17 )
1:23pm April 2nd 2017 via Hootsuite
Chin: And initial exposure to https://t.co/OAQvbLpb5H data, initial accuracy 60% but rises over subsequent iterations. #AACR17
1:22pm April 2nd 2017 via Hootsuite
Chin: Shows training and test set methodology, and comparison to SME (subject matter expert - experienced oncologist) #AACR17
1:21pm April 2nd 2017 via Hootsuite
Lynda Chin (MD Anderson TX) Big data and AI: developing cognitive applications in medicine #AACR17
1:20pm April 2nd 2017 via Hootsuite
Sawyer: Looked at transcription factors in Mu et al paper, SOX2 reprogramming factor. Across models, SOX2 changed after knock-down #AACR17
12:24pm April 2nd 2017 via Hootsuite
