Impact AI

Leveraging AI to prevent heart attacks and strokes offers a significant opportunity to transform healthcare and make it more productive, personalized, and accessible. Joining me today is Todd Villines, the Chief Medical Officer of Elucid, a pioneering medical technology company at the forefront of AI-powered heart attack and stroke prevention.We discuss how Elucid's FDA-cleared product uses cutting-edge AI to analyze and characterize arterial plaque through CT scans and the innovative aspects of Elucid's algorithms. We explore the role of machine learning in Elucid's technology, from identifying risky plaques to using fractional flow reserve derived from CT without invasive procedures.Tuning in, you’ll learn about the importance of high-quality data annotation and the rigorous validation process required to ensure accuracy across various scenarios and demographics. We also unpack the company's approach to annotating data, avoiding bias, and using diverse data sets. To discover how Elucid is making strides in cardiovascular health and paving the way for a healthier future, don’t miss this conversation with Todd Villines!Key Points:Todd’s professional background and why he joined the team at Elucid.Elucid’s mission and how it leverages AI for medical technology.The role of machine learning in Elucid's technology.Developing a fractional flow reserve derived from CT analysis.Why data annotation is crucial for training the Elucid models.The importance of validation and how Elucid ensures the accuracy of its product.Challenges and limitations of working with CT images.How the company’s technology integrates into the existing clinical workflow.Metrics used to assess the impact of Elucid's technology.Intelligent design, diverse datasets, and avoiding bias in AI development.Discover Elucid's future outlook and its plans to expand.Quotes:“We’ve created proprietary algorithms that were trained by histology using traditional image processing techniques to recognize different types of plaque based on histology.” — Todd Villines“In the field of medical imaging, using supervised machine learning and annotated data of very high quality and also generalizable to the clinical use case of your technology is vitally important.” — Todd Villines“You can’t just go out and pick the very highest image quality to train your models or you’re going to end up with a very overfitted model that doesn’t generalize to the clinical use case.” — Todd Villines“Just like any good clinical study, designing your AI technology is probably the most important thing. Spend the time upfront to get it right.” — Todd VillinesLinks:Todd Villines on LinkedInTodd Villines on XElucidResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Measuring plant health is essential for various applications, such as agriculture and conservation biology. Being able to measure plant health effectively enhances resource use, mitigation measures, and sustainable use of ecosystems in a rapidly changing world. But how is this done?In this episode, I am joined by Kevin Lang, the CEO and president of Agerpoint, who shares insights into their cutting-edge solutions for measuring and monitoring plants. Agerpoint is a company that provides tools and solutions for measuring and monitoring plants to gather accurate data related to forests and crops. Their spatial intelligence platform is designed to unlock valuable insights for sustainable food systems and climate solutions.In our conversation, Kevin explains how Agerpoint harnesses the power of AI, machine learning, and 3D modeling to enhance crop management, reduce resource inputs, and promote regenerative farming practices. Learn about the innovative ways Agerpoint is leveraging existing technology, such as smartphones, to make their products more accessible and affordable. Kevin also delves into the types of data Agerpoint uses, the validation process, the challenges of analyzing plant health, and much more. Tune in as we explore the fusion of technology and nature and how it's helping shape a more sustainable and efficient future with Kevin Lang from Agerpoint!Key Points:Kevin’s professional background and the road to Agerpoint.Details about Agerpoint and what the company specializes in.How machine learning forms the core of Agerpoint’s technology.The range of data modalities Agerpoint uses for its technology.Data challenges and insights into the validation process.Bridging the gap between technology and biology.Agerpoint's approach to recruiting top talent.Measuring the impact of Agerpoint’s technology.Essential advice for AI startups: align with your investors, board, and team.What to expect from Agerpoint in the future.Quotes:“The combination of the point cloud and the machine learning and automation and then putting this all together in a cloud-based system, where we can fuse these data layers together, is unique.” — Kevin Lang“Machine learning really plays a critical role across multiple processes and products in our business, and it’s really the core of the Agerpoint platform.” — Kevin Lang“Validation is just as much of a scientific challenge as it is a change management and communication challenge with your clients.” — Kevin Lang“The impact [of our product] is about access and affordability.” — Kevin Lang“We are building a company and a capability that we believe represents the next wave of digital agriculture and forestry.” — Kevin LangLinks:Kevin Lang on LinkedInKevin Lang on XAgerpointAgerpoint Capture iOS AppKnow Your CarbonResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Burnout in the medical field is a significant and pervasive problem that affects healthcare professionals across various specialties and levels of experience. In this episode, I explore the impact of artificial intelligence on the field of radiology. My guest, Jeff Chang, the Co-founder and Chief Product Officer of Rad AI, shares his insights into the transformative power of AI in addressing critical challenges in radiology (like burnout) and improving patient care.Discover Rad AI's groundbreaking products, including Rad AI Omni Impressions and Continuity, driven by powerful machine learning, and learn how they seamlessly integrate into clinical workflows. Jeff also speaks about measuring the impact of their products, exciting future applications for their products, Rad AI's vision for the future of global diagnostic care, and much more! Don't miss this deep dive into AI's potential in revolutionizing radiology and improving patient lives worldwide with Jeff Chang from Rad AI!Key Points:Background on Jeff and the overall mission of Rad AI.Insight into the next-generation products that Rad AI offers.How machine learning plays a central role in their products.Training data and the use of transformer models in Rad AI's solutions.Certain limitations and hurdles of working with radiology reports.Ensuring product integration with existing clinical workflow.Exciting future applications for AI and machine learning in the space.Ways that Rad AI measures the impact of its technology.Essential advice for leaders of AI-powered startups.Upcoming products from Rad AI and the company’s future impact.Quotes:“Every one of our products is centered around the latest machine learning transformative work.” — Jeff Chang“There is absolutely zero change to the existing workflow. That makes it really easy for radiologists to adopt without having to change anything that they currently do.” — Jeff Chang“The more you streamline both deployment and the training process, getting radiologists or your users used to using the product, the easier it becomes and the more time you save for the radiologist.” — Jeff Chang“Because of the post-processing, because of the specific training on the radiologist’s historical reports, [our models] are much more accurate than the current state of the art [LLMs] for the applications that we currently provide.” — Jeff ChangLinks:Jeff Chang on LinkedInRad AIRad AI on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Indra den Bakker, CEO and Co-founder of Overstory, discusses how they use machine learning and satellite imagery to revolutionize vegetation management near power lines. They navigate challenges like varying resolutions and lighting conditions to provide accurate insights. Indra shares advice for AI startups and highlights the transformative power of AI in solving real-world problems for a more sustainable future.

Can machine learning improve the treatment of neurological diseases? Here to tell us how AI is improving the landscape of neurological care is Dirk Smeets, Chief Technology Officer of icometrix.We kick off our conversation considering what the landscape of treatment looks like today before exploring the role of AI in matching treatment to technologies. We discuss the parallels between the outcome of ChatGPT and the implications of neurological imaging, and Dirk reveals how icometrix has been able to produce artificially intelligent machines that can carry out expert tasks. Imagining the future, we discuss different approaches to adapting 2D imaging and the advantages of taking a deep-learning approach. This episode covers the process of choosing focus areas, weighing different feature requests, the influence of the regulatory process, and Dirk’s predictions for the future of neurological treatment. Join me today to hear all this and so much more. Key Points:Introducing Dirk Smeets, Chief Technology Officer of icometrix. The work of icometrix in treating neurological disorders.What the landscape of neurological disease treatment looks like today.The role of AI in matching treatment to technologies.Parallels between the outcome of ChatGPT and neurological imaging.Introducing artificially intelligent machines that can carry out tasks to the level of experts.How expert-level AI care will change the way care is carried out in the coming years.How icometrix uses machine learning in the analysis of imaging data.Different approaches to adapting the standard process of 2D imaging through deep learning.Advantages to this deep-learning approach.Obstacles to developing diagnosis capabilities through machine learning.Using clinical workflow to determine which areas to focus on with new innovations.Choosing to embed automation into the AI process to empower practitioners.Weighing different feature requests when choosing which to develop.How the regulatory process impacts machine learning developments.Building trust by publishing work for the public eye.Dirk’s recommendation for other founders in AI technology. Thinking of AI as the means to achieve a goal rather than the purpose. His prediction for the future of treatment for neurological conditions and the role icometrix will play. Quotes:“One in three people will suffer in their life from a neurological condition. The societal burden for neurological conditions is the sum of kidney disease, heart disease and diabetes together.” — Dirk Smeets“The field of neurological conditions is moving. There are treatments available, but the downside unfortunately, is that those medications are not working for everyone. It is still a lot of trial-and-error.” — Dirk Smeets“We can build machine learning models that can do tasks at the level of experts. For example, expert radiologists. That will change the way we do current practice.” — Dirk Smeets“At icometrix we find science important. It's actually almost in our DNA. The reason why is that we believe that the technology we build should be scientifically sound.” — Dirk SmeetsLinks:Dirk Smeets on LinkedInDirk Smeets on Twittericometrixicometrix on LinkedInicometrix on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

As the agricultural industry expands to meet increased population growth and food demand, food security becomes a matter of global importance, which is why today’s guest is using AI to help farmers optimize the health of their farms.Benji Meltzer is the Co-founder and CTO of Aerobotics, a South African Ag-Tech startup focused on providing crop protection to farmers through early problem detection and alerts. Combining satellite data, drone imagery, and scout information, Aerobotics tracks farm performance on a tree-by-tree basis and uses machine learning (ML) to identify early-stage problems, automatically detect pests and diseases, guide farmers to these locations, and suggest solutions.In this episode, Benji offers some deeper insight into what Aerobotics does and how they can help farmers optimize the yield of their orchards. We also discuss how they use ML to process vast amounts of complex data, the challenges they encounter in the field, and Benji’s advice for other AI startups who hope to solve real-world problems, plus so much more. For a fascinating conversation about the applications of AI in agriculture, tune in today!Key Points:An overview of Benji’s interests, formal education, and what led him to co-found Aerobotics.What Aerobotics does and how it contributes to more sustainable agriculture.The role that ML plays in Aerobotics’ technology.How they gather and annotate the data needed to train different models.Challenges they have encountered, from connectivity issues to weather conditions.Ensuring that Aerobotics’ models can generalize to many different variations.Why there is no one-size-fits-all approach to developing these models.Steps to planning and developing new ML products or features.How the seasonal nature of agriculture impacts Aerobotics’ ML development.Benji’s advice for leaders of AI-powered startups: keep it simple!What the future holds for Aerobotics and how they hope to expand within their niche.Quotes:“We're using the performance of the crop to inform how we farm and becoming more responsive and reactive rather than farming completely preventatively.” — Benji Meltzer“The role that Aerobotics is playing is building that layer of insight and understanding into how the crop is performing to enable people to make these decisions.” — Benji Meltzer“At its core – this product wouldn't exist without machine learning.” — Benji Meltzer“Where AI can add the most value is in using technology to reduce that complexity and to downsample and simplify information into patterns and decisions that people can actually consume. It's almost too easy to compound that complexity and not actually solve the underlying problems.” — Benji MeltzerLinks:AeroboticsBenji Meltzer on LinkedInBenji Meltzer on XResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today on the Impact AI podcast, I am excited to host Victor Hanson-Smith, Head of Computational Biology at Verge Genomics. He joins me today to talk about the unlocking of new drugs for neurodegenerative diseases and their mission at Verge to make drug discovery cheaper and faster.In our discussion, Victor tells about current Verge ventures and the important part they have in developing new drugs, the role machine learning plays, and the type of data sets they work with.  We also hear about the complexity behind “omics” data and how Verge is validating machine learning models. Victor talks passionately about the importance of team building, leadership, and company culture, and the vital role they have in establishing effective machine learning models. To hear his advice to other leaders of AI-powered startups, including the three races underway, tune into today’s episode. Key Points:How Victor ended up at Verge Genomics as Head of Computational Biology.How his experience with his father’s disease ignited a curiosity in him.Verge Genomics’ ventures and why it is important in developing new drugs.The role machine learning plays in their technology and approach. Victor elaborates on the types of data they work with in the different models.More about the Human Data Atlas and how it works.He talks about the Verge Genomics model setup and functionality.Different challenges they’ve encountered with human omics data and machine learning.How they ensure building the most effective models using complex “omics” data.Verge’s validation process for machine learning models. How generative AI has influenced (or not influenced) advancements at Verge.Advice from Victor to other leaders of AI-powered startups.Where Victor sees the impact of Verge Genomics in three to five years.Quotes:“We like to say that Verge Genomics is a full-stack drug discovery and development company.” — Victor Hanson-Smith“This revolution in systems biology has the potential for new treatments for countless human diseases and has the potential to make drug discovery cheaper and faster. Long-term, it might even transform our fundamental relationship with the concept of disease.” — Victor Hanson-Smith“One of the key differentiators for Verge is that we base our discoveries in human data.” — Victor Hanson-Smith“At Verge, we often say, to succeed in humans, we start in humans, and so we go direct to the source.” — Victor Hanson-Smith“We believe that no single data set or a single piece of data is sufficient for the sorts of rigorous drug discovery we’re interested in. Rather, our platform combines lots of different data types and layers and we’re looking for signals that are consistent across those layers.” — Victor Hanson-Smith“This problem of finding the right targets, I think, is existential and one of the most upstream problems for the drug discovery industry and that’s a problem right now I don’t think is crackable by generative AI but Verge is on the frontlines of getting us closer there.” — Victor Hanson-SmithLinks:Victor Hanson-Smith on LinkedInVictor Hanson-Smith on TwitterVerges GenomicsConverge PlatformResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I sit down with Abhilasha Purwar, founder and CEO of Blue Sky Analytics, to explore the groundbreaking realm of climate intelligence derived from satellite data. Abhilasha's captivating journey, from engineering to environmental research and policy consulting, reveals her passion for addressing climate change through data and technology. Blue Sky Analytics is on a mission to bridge the gap between satellite data and actionable insights, monitoring everything from carbon projects to wildfire risks and infrastructure assets.Discover the pivotal role of machine learning in analyzing vast amounts of satellite imagery and how it's transforming our ability to measure and combat climate change with precision. Abhilasha shares compelling examples of Blue Sky Analytics' models, from monitoring forests to assessing biodiversity and air quality. We dive into the challenges of satellite data procurement and the importance of open data and open source in advancing climate solutions. Find out how Blue Sky Analytics measures its impact, learn valuable advice for AI startup leaders, and get a glimpse of the inspiring future where all forests and lakes become digital public assets worldwide. Tune in now to discover the power of satellite data with pioneer Abhilasha Purwar!Key Points:Abhilasha's background and her journey to founding Blue Sky Analytics.Blue Sky Analytics and how the company is helping combat climate change.Discover the role of machine learning at Blue Sky Analytics.Exciting applications of the Blue Sky Analytics models.The challenges and hurdles of relying on remote sensing data.Hear why open data and open-source software are essential.How Blue Sky Analytics plans to bridge the paywall gap.Fascinating and potential future applications of satellite data.Insights into the single performance indicator Blue Sky Analytics uses.She shares key advice for leaders of AI-powered startups.The vision that Blue Sky Analytics has for the future.Quotes:“What Blue Sky really does is effectively monitor the pulse of the planet.” — Abhilasha Purwar“Objectivity and numbers ground us and they serve as some sort of truth and some sort of objectivity against all kinds of these emotionally-driven debates.” — Abhilasha Purwar“What machines are able to do in one day? It would take you and I 10,000 years or something to do.” — Abhilasha Purwar“The bottleneck of building out that trust within the community, building out that trust for the community with other stakeholders, can really be solved if the community was to collaborate with each other.” — Abhilasha PurwarLinks:Abhilasha Purwar on LinkedInAbhilasha Purwar on XBlue Sky AnalyticsResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

New research and technology are radically transforming cancer treatment, and, today, we find out how. I am joined by Genialis CEO and Co-Founder, Rafael Rosengarten to discuss his company’s mission to “outsmart cancer.” Genialis is revolutionizing cancer care by developing AI models that decode the biology behind different types of cancer and identify the most effective therapies for individual patients.In this episode, we discover how Genialis’ innovative approach of turning RNA sequencing data into tumor phenotype classification is remolding the landscape of precision medicine. Rafael explains the company’s methods of handling the high dimensionality and sparseness of sequencing data while addressing bias issues, filling us in on why they use shallower artificial intelligence architectures for algorithm training and more. Join us as we explore the cutting-edge world of personalized cancer treatments that are shaping the future of oncology.Key Points:Genialis CEO and Co-Founder, Rafael Rosengarten’s background; what led him to Genialis.How Genialis applies machine learning to help patients find personalized cancer treatments.Their collaboration with drug and diagnostics companies to deploy their models.How the models use RNA sequencing data to predict and classify tumor phenotypes.The challenges encountered when training models with sequencing data.Rafael defines sequencing data.Why RNA sequencing for clinical applications is considered cutting-edge.Genialis’ methods for handling the high dimensionality and sparseness of sequencing data.The various sources of bias and how they have addressed these issues.Why they use shallower artificial intelligence architectures for algorithm training.How the FDA’s regulatory process affects how Genialis develops and validates its models.The benefits the Genialis team has seen from publishing research articles.How they measure the impact of their technology.Rafael’s advice to other leaders of AI-powered startups.The hyper-commoditization of AI technologies.Rafael predicts the future impact of Genialis and shares his goals for the company.Quotes:“[Genialis applies] machine learning to try to help patients find the best drugs for their disease, to help realize the promise of precision medicine.” — Rafael Rosengarten“The models are learning the fundamental biological nature of the disease. From that, we can extrapolate what the best intervention will be.” — Rafael Rosengarten“Not all genes have detectable expression at once. Certainly, not all genes are going to be informative. We've built really beautiful software that allows us to aggregate these kinds of sequencing data, to process them in a very uniform way.” — Rafael Rosengarten“It really is a pan-cancer model, even though it was trained on a data set that was just gastric cancer. And it works on RNA sequencing of all different chemistries, even though it was trained on microarray” — Rafael Rosengarten“The key with algorithm training, of course, is to try to avoid what's known as overfitting.” — Rafael Rosengarten“Every phenotype that our model predicts whether it's phenotype A, B, C, or D, has a different therapeutic hypothesis.” — Rafael Rosengarten“AI technologies right now are becoming hyper-commoditized.” — Rafael Rosengarten“It is still possible for small companies to come up with really innovative algorithms — but for the most part, it really matters how you deploy these technologies.” — Rafael RosengartenLinks:Rafael Rosengarten on LinkedInRafael Rosengarten on TwitterGenialisGenialis on LinkedInGenialis on TwitterTalking Precision Medicine PodcastResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

The accelerated development of medical AI could be life-changing for patients. Unfortunately, accessing large amounts of diverse, standardized data has been a major stumbling block to progress. That’s where Segmed comes in, a platform that allows researchers to access diverse, high-quality, and de-identified medical imaging data. Crucially, Segmed’s platform also provides data for medical AI training and validation.I am joined today, by Segmed’s co-founder, Jie Wu, to discuss how they are solving key data issues to rapidly accelerate medical AI development. You’ll hear Jie break down some of the biggest challenges in curating medical image datasets — including the extra computational power needed to handle high-res medical images, like CT scans — and how they are addressing these obstacles. Jie also takes the time to emphasize the need for diversity when curating medical image datasets and the importance of mitigating bias during the data curation phase. To learn more about Segmed and how they are contributing to the development of medical AI, be sure to tune in today!Key Points:A warm welcome to Jie Wu, co-founder of Segmed.Insight into how Segmed is solving data issues to accelerate medical AI development.Why solutions to these data issues are crucial for medical research.Segmed’s focus on medical imaging data.Their approach to different imaging modalities.An overview of the key challenges in curating medical image datasets.How Segmed determines the amount of data they will need.Best practices for curating a training set of medical images.Why collecting a diverse range of images is essential.An overview of how the quality of labels is assessed by experts.How imaging modality influences Segmed’s approach to creating datasets.The variations in datasets across different imaging pathologies.Special considerations that inform the validation set versus the training set.How bias manifests in models trained on medical images.Steps that can be taken to mitigate bias during the data curation phase.How the need for diverse datasets has increased along with greater awareness of bias.Jie’s thoughts on the future of foundation models in the medical AI space.Quotes:“A high-resolution of CT can take up to several gigabytes of storage itself.” — Jie Wu“I think the most important piece is actually to collect as diversely as possible. So I ask that given the budget limit or maybe time limit, the size of the data set will be limited but it should be at least representative of the target population and targeted practice.” — Jie Wu“The best quality labels are curated by experts and it is curated by multiple experts.” — Jie Wu“A 3D image stores much more information than the 2D images, so you need less data for that.” — Jie Wu“The external validation datasets require much more carefully curated datasets and much higher quality labels, and also it needs to be representative of the population, of the institutions, and also geographical locations.” — Jie Wu“We hope that we can enter into the development of AI and make these algorithms go to market faster and benefit more people.” — Jie WuLinks:Jie Wu on LinkedInSegmedResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.