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"Dean, Jeff"
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Deep learning-enabled medical computer vision
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
Journal Article
Customization scenarios for de-identification of clinical notes
Background
Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets.
Objective
We present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized.
Methods
We implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset.
Results
Fully customized systems remove 97–99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems.
Conclusion
Health organizations should be aware of the levels of customization available when selecting a de-identification deployment solution, in order to choose the one that best matches their resources and target performance level.
Journal Article
A guide to deep learning in healthcare
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.
Journal Article
A graph placement methodology for fast chip design
Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research
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, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area. To achieve this, we pose chip floorplanning as a reinforcement learning problem, and develop an edge-based graph convolutional neural network architecture capable of learning rich and transferable representations of the chip. As a result, our method utilizes past experience to become better and faster at solving new instances of the problem, allowing chip design to be performed by artificial agents with more experience than any human designer. Our method was used to design the next generation of Google’s artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation. Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.
Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.
Journal Article
First Person
An interview with Jeff Dean, head of artificial intelligence at Google is presented. Dean said that we've seen significant developments in deep learning--essentially, a rebranding of artificial neural networks. These have been around for 30 or 40 years as a way of describing abstract ways of learning from interesting inputs and outputs. But now it turns out that deep learning is useful for all kinds of problems in the fields of computer vision, speech recognition, language understanding, and language translation. Most successful kinds of machine learning are of that form: Collect a large data set of inputs and outputs that you care about. It might be a bunch of pictures, and each picture is labeled with that's a truck that's a pigeon, that's a particular kind of monkey. As people take advantage of the many new online services out there, they create data about how those services are being used.
Journal Article
The Key Benefits of FRACTIONAL LEADERSHIP
Having a fractional Chief Procurement Officer (CPO) offers significant advantages for organizations, especially in industries like the fastener sector where strategic sourcing, cost control, and supply chain flexibility are crucial. A fractional CPO provides executive-level procurement leadership on a part-time basis, delivering expert insights and strategies without the cost and commitment of a full-time hire. Key benefits include cost savings by avoiding the fixed costs of a full-time CPO while accessing top-tier expertise. Fraction al CPOs bring deep procurement experience to identify cost reduction opportunities, negotiate improved contracts, and implement tailored sourcing strategies aligned with business goals. They also improve supplier relationship management and risk mitigation--vital for maintaining resilient supply chains in dynamic markets. Their flexibility and scalability enable them to adjust involvement as business needs evolve, speeding implementation of best practices and enhancing compliance and sustainability efforts, including meeting ESG and vendor requirements.
Trade Publication Article
The Artificial Intelligence- Powered Transformation of Procurement and Supply Chain in the Fastener Industry
Procurement and supply chain teams are vital to any manufacturing-driven business, yet their impact is often underestimated. These professionals manage a vast array of responsibilities, from project management and contracting to risk mitigation, cost control, and accounts payable. Their work touches every corner of the organization, linking the need state (the identification of a requirement for goods or services) all the way to financial reporting.
Trade Publication Article