Author: torontoai
Senior Computer Vision Engineer – indus.ai – Toronto, ON
From indus.ai – Tue, 16 Jul 2019 23:27:52 GMT – View all Toronto, ON jobs
[P] Need an Interview for Article I’m writing.
Hey, I’m writing an article about machine learning and how its changing how microloans are available to millennials. I am just curious if I could interview anyone in this sub with at least college or job-level experience in data science/machine learning or in microloans for about 15 min over the phone this week. Thanks
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[P] ELMo from scratch?
In one of my projects I need to train ELMo embeddings. AllenNLP has an implementation of this but I thought I’ll take this opportunity to implement it from scratch.
I always wanted to develop the skill to replicate the result of research papers and experiment with them. So I think implementing this from scratch will give me a kick start. Also, I’ll be able to learn a lot about PyTorch.
I already read the paper of ELMo, along with Character-Aware Neural Language Models, Highway Networks, really cool papers!
I’m pretty sure you passed the stage where I am at right now. So it would be tremendously helpful if you could share your opinion, experience and suggestions.
TIA
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[D] Image recognition on server
Hello!
I’m trying to create an image recognition functionality, where the user takes photos and sends it to my server and I check if the photo contains an image I previously registered to my server.
I found out about a paper and its implementation
https://github.com/cl199443/Deep-Semantic-Feature-Matching
I currently think feature-matching is what I look for.
Question it, is there a automatic way to train a feature network to also store feature patterns it’s seen before and matched to target images?
Or do you have any other workflow suggestions to tackle this problem? Thanks
Edit: I need this solution to be a zero-shot learning solution.
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[P] TensorFlow DICOM Medical Imaging Decoder Operation
Hello, I wanted to share something our team has been working on for a while. I work on an early stage radiology imaging company where we have a blessing and curse of having too much medical imaging data. Something we found internally useful to build was a DICOM Decoder Op for TensorFlow. We are making this available open-source here: https://github.com/gradienthealth/gradient_decode_dicom.
DICOM is an extremely broad standard, so we try to cover the 90% case of image formats (PNG, TIFF, BMP, JPEG, JPEG2000). We also support multi-frame/multi-frame color images. Try images found here: https://barre.dev/medical/samples/. In the case an unsupported format is found, an empty Tensor is returned which can be filtered out. Reading the files directly off of bucket storage has allowed us to prevent data duplication of .dcm data (a single CT can be 300MB). You can play with the op in this Colab notebook: https://colab.research.google.com/drive/1MdjXN3XkYs_mSyVtdRK7zaCbzkjGub_B
We firmly believe that having open-source resources in healthcare is what will enable its use in practice, not AI trade secrets. We plan on opening more of our work in the future. DM me if there is interest in contributing to upcoming toolkits (the next one we are thinking of creating is an operation to decrypt+decompress gzip files). Also, lmk if there is interest in working with our dataset (~300M DICOMs + notes). The goal of these project collaborations is that they are ultimately open-sourced.
Anyway, give the operation a try. If there are problems with loading a file of interest, please make an issue on GitHub. Right now only Linux based systems are supported, and a Dockerfile example will be coming soon.
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Evening the Odds: Cornell’s STORK AI Tool Evaluates Embryo Candidates for Better IVF
There’s less than a 50 percent chance that a round of in vitro fertilization — one of the most common treatments for infertility, running up to $15,000 — will succeed. But those odds could be dramatically improved with an AI tool developed by researchers at Cornell University.
Introduced in 1978, IVF is a process through which eggs are fertilized with sperm in a lab, creating multiple embryos that can be transferred into a patient’s uterus. Clinics monitor embryo development to pick the highest-quality embryos for transfer, improving the odds of pregnancy.
Still, less than half of transferred blastocysts (embryos that have grown for around five days) successfully implant in a patient’s uterus, according to the CDC. That figure drops below 15 percent for patients over the age of 40.
Trained and tested on a dataset of over 10,000 time-lapse images of human embryos, Cornell researchers created an AI model dubbed STORK that uses convolutional neural networks to analyze embryo growth and evaluate which candidates are most likely to lead to successful implantation.
To increase the probability of pregnancy, clinics often transfer multiple embryos at once. And that carries risks.
“This can lead to twins, triplets and other multiples, which adds to the complications,” said Iman Hajirasouliha, assistant professor of computational genomics at Weill Cornell Medicine. “If we can reliably predict the implantation success rate based on an algorithm, then we can limit the number of transfers.”
Betting on the Best Embryo Candidate
Over 2.5 million cycles of IVF are performed each year, resulting in around 500,000 births. For each of these cycles, the task of choosing which embryos are most likely to result in a successful pregnancy lies with a team of embryologists.
These experts manually grade the developing embryos based on time-lapse images — a time-consuming and subjective evaluation. With no universal grading system, there’s little agreement among embryologists on which are the best embryo candidates.
The scientists developing STORK found that a panel of five embryologists unanimously agreed less than 25 percent of the time on whether an embryo was high, fair or low quality.
In contrast, STORK’s predictions agreed with the embryologist panel’s majority vote more than 95 percent of the time — suggesting that the tool may outperform individual embryologists and bring better consistency to the embryo evaluation process.
AI is also much faster at analyzing the image data. A clinic that treats around 4,000 people a year may have three embryologists manually evaluate embryo candidates for each patient. STORK can evaluate embryo candidate quality for 2,000 patients in just four minutes.
The Cornell researchers developed the deep learning model using the TensorFlow framework and four NVIDIA GPUs, accelerating the training process up to 4x over CPUs.
So far, the scientists have tested their tool on embryo images from clinics in New York, Spain and the United Kingdom. They hope any IVF facility that collects time-series images of embryos could use the tool.
However, embryo quality is just one clinical factor behind IVF success rates. Patient age is a key variable affecting the probability of implantation — and the likelihood of a healthy full-term pregnancy.
To better assess the rate of successful pregnancy and live birth, the researchers have developed a decision tree model that incorporates STORK’s embryo quality analyses as well as patient age data.
The post Evening the Odds: Cornell’s STORK AI Tool Evaluates Embryo Candidates for Better IVF appeared first on The Official NVIDIA Blog.
Thousands of Images at the Radiologist’s Fingertips Seeing the Invisible
Vector’s Second Pathfinder Project to Enhance Radiology with AI
Toronto – Today, the Vector Institute, an independent, not-for-profit research institute focused on leading-edge machine learning, announced the second in its series of Pathfinder Projects to implement Artificial Intelligence (AI) in the health sector.
The second Pathfinder Project, performed in partnership with the University Health Network (UHN) and the University of Waterloo (UWaterloo) will enhance radiology diagnoses with AI.
Coral Review, a software solution developed at UHN, is a peer learning tool used by clinicians in diagnostic imaging to support continuous quality improvement of radiologist practice. Using an algorithm developed by Dr. H.R. Tizhoosh, Director of the Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) at UWaterloo and a Faculty Affiliate at the Vector Institute, an AI-enabled Coral Review would scan through thousands of existing medical images (i.e., x-rays) for ones similar to a patient’s and recommend a diagnosis to the attending physician.
“Coral Review currently enables anonymous peer reviews of medical imaging diagnoses. However, it is limited by the availability of physicians who perform the review or ‘second opinion’,” says Leon Goonaratne, Senior Director of Information Technology, UHN. “An AI-enabled peer review solution has the ability to provide the physician with more information when they perform the review, including the identification of images corresponding to rare or difficult to see cases”.
Pathfinder Projects are small-scale efforts designed to produce results in 12 to 18 months that guide future research and technology adoption. With technical and resource support from the Vector Institute, the projects each bring together a multidisciplinary research team to tackle an important health care problem or opportunity using machine learning and AI more broadly. Each project was chosen for its potential to help identify a “path” through which world-class machine learning research can be translated into widespread benefits for patients.
About the Vector Institute
The Vector Institute is an independent, not-for-profit corporation dedicated to advancing artificial intelligence, excelling in machine and deep learning. The Vector Institute’s vision is to drive excellence and leadership in Canada’s knowledge, creation, and use of AI to foster economic growth and improve the lives of Canadians.
The Vector Institute is funded by the Province of Ontario, the Government of Canada through the Pan-Canadian AI Strategy administered by CIFAR, and industry sponsors from across the Canadian economy.
About University Health Network
University Health Network consists of Toronto General and Toronto Western Hospitals, the Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, and The Michener Institute of Education at UHN. The scope of research and complexity of cases at University Health Network has made it a national and international source for discovery, education and patient care. It has the largest hospital-based research program in Canada, with major research in cardiology, transplantation, neurosciences, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. University Health Network is a research hospital affiliated with the University of Toronto. www.uhn.ca
AI-Enhanced Coral Review
Dr. H. R. Tizhoosh and his team have worked at the nexus of health care and artificial intelligence (AI) for over a quarter century. Yet, only now is the world beginning to see the fruits of that labour. “In spite of the progress we’ve made,” he says, “we’re at the very beginning if we want to bring the technology into hospitals.”
Director of Kimia Lab at the University of Waterloo (UWaterloo), Dr. Tizhoosh will be at the forefront of this important shift as he seeks to enhance University Health Network’s (UHN) medical imaging peer review system, Coral Review. It is the second of the Vector Institute’s Pathfinder Projects, which bring together multidisciplinary research teams to tackle important health care problems using machine learning.
Developed at UHN, Coral Review has been implemented at a number of hospitals across Ontario. Designed to bring focus to quality and education within medical imaging departments, the solution enables an anonymous peer review of a medical imaging diagnosis, as well as image quality.
“Coral Review has enabled a program of quality and education for many hospitals,” says Leon Goonaratne, Senior Director of Information Technology, UHN. “While this peer review process is helping identify and facilitate many learning and coaching opportunities across the province, we believe artificial intelligence is the next step to making the solution even more effective”.
To bring more regularity and efficiency into the system, Dr. Tizhoosh and his team are training a machine learning algorithm with a mixture of public and private data set of over 200,000 anonymized medical images. Once trained, the AI-enhanced Coral Review application would find similar looking images from past cases and offer suggested diagnoses, while leaving the final decision to doctors.
“It’s AI deployed in a slightly different way,” says Dr. Tizhoosh. “It allows the radiologist making the diagnosis to benefit from the knowledge of thousands of diagnoses made by other clinicians. That’s very different from making a diagnosis from scratch.”
The teams at UHN and Kimia Lab are starting relatively small, focusing on chest x-rays and specifically looking at pneumothorax, or collapsed lungs. The condition is a technical challenge for radiologists and a practical one for doctors; certain types can be difficult to see on an x-ray and a collapsed lung is both painful and potentially fatal. Small collapses pose a particularly significant challenge. “Doctors can miss small collapses in 40 percent of cases because you just can’t see it,” says Dr. Tizhoosh.
As it currently stands, their algorithm has about a 70 percent accuracy rate. But with technology and resources support from Vector they will fine tune it over the next year and hope to push that rate above 90 percent before incorporating it into the existing system. Dr. Tizhoosh also hopes to expand the project’s scope beyond pneumothorax. “Long term, we want to add a long list of problems that we automatically check,” he says. “We want to find more difficult problems and work on a larger scale in the radiology domain.”
Once implemented, the system will be the first of its kind: an AI-enabled diagnostic tool for medical images based on image retrieval. “Working with hospitals to implement AI in medical imaging is the most thrilling thing I have ever done in my career,” Dr. Tizhoosh enthuses. “I want to look back and say, ‘this is what I did as a computer scientist.’ It’s a very exciting time.”
AI-Enhanced Coral Review is the second in a series of Pathfinder Projects identified and supported by the Vector Institute.
[D] How does Calorie Mama work? Python implementation
How do you train a model to learn nutritional information from a picture of a food, or drink? Maybe you need a start with an image classifier, like how the app Calorie Mama works. Despite reports that nutritional recognition isinaccurate and unreliable, I’m still interested in the (python) implementation of ID’ing the food, getting calories, nutrients, vitamins etc.
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