PathAI’s Post

We're pleased to be named to Boston Business Journal's list of the largest #HealthTech companies in Massachusetts alongside some of the area's other finest innovators! #Pathology #AI https://lnkd.in/ezEaUcYd

🌐 Real-World Data that includes #clinical, #molecular, and #pathology data has a unique ability to drive innovation throughout the #drugdevelopment lifecycle. With these #multimodal datasets, researchers and drug developers can gain deeper insights into disease mechanisms, #biomarker strategies, and novel therapeutic targets through comprehensive, multi-scale analyses; ultimately making drug development faster and more cost-effective to improve patient outcomes. There are many different data modalities that can be utilized to accelerate research and #drugdevelopment. In our latest #blog, we dive into the unique benefits each of these modalities offer: https://lnkd.in/e3at47wQ Key Components of #RWD: 🔍 Pathology Data: Advances in #digitalpathology and AI, like PathAI’s PathExplore™, turn routine pathology samples into rich, quantitative cell and tissue insights. 💊 Clinical Data: Clinical RWD captures the complete patient journey, including treatment patterns, adverse events, and #patient outcomes. 🧬 Molecular Data: Genomic, transcriptomic, and proteomic data provides a comprehensive picture of disease mechanisms and biomarker signatures. Are you ready to unlock and boost your real-world data with #AI-powered pathology? Learn more about the power of AI-powered pathology + RWD and view our resources: https://lnkd.in/e8KH6Fzz #RealWorldData #biotech #translationalresearch #drugdevelopment #multimodal #RWD #BigData #MachineLearning #AI #DeepLearning #Healthtech #biopharma PathExplore™ is for research use only. Not for use in diagnostic procedures.

🌐 Real-world data (#RWD) is revolutionizing #healthcare research, #biomarker discovery, #drugdevelopment and more. But RWD has traditionally lacked a crucial data modality: 𝘩𝘪𝘴𝘵𝘰𝘱𝘢𝘵𝘩𝘰𝘭𝘰𝘨𝘺. In our latest blog, we detail how #pathology can be enhanced with #AI and paired with clinico-genomic RWD to unlock powerful new disease insights. Read the blog 📝 https://lnkd.in/eA8zf7dg Despite its importance, the size and complexity of pathology data have made it difficult to analyze quantitatively at scale. However, advancements in #digitalpathology and AI-driven tools are transforming this landscape. By converting unstructured whole slide images into structured data outputs, pathology can be seamlessly integrated with clinico-genomic RWD to deliver even richer multimodal patient cohorts. Whether you are a pathologist, researcher, or simply curious about the intersection of data science and healthcare, read our latest blog to learn more about applications and opportunities for AI-powered pathology in RWD. Learn more about the power of AI-powered pathology RWD and view our resources: https://lnkd.in/e8KH6Fzz #RealWorldData #biotech #translationalresearch #drugdevelopment #precisionmedicine #genomics #DNA #transcriptomics #cancerresearch #realworlddata #RWD

Next week, PathAI scientists Sean Grullon and Marc Thibault will be presenting at four workshops at [ICML] Int'l Conference on Machine Learning July 21-27 in Vienna, Austria! View the program: https://lnkd.in/eTNqB8i6 If you’re attending the conference, stop by the workshops and hear how #MachineLearning was used to develop the new #pathology-centric #FoundationModel PLUTO for quantitative histopathology at scale and learn more about how it was used to reveal deeper biological insights. Read the abstracts and add the workshops to your schedule below. 1. #PLUTO: Pathology Universal Transformer Pathology images provide a unique challenge for computer-vision-based analysis: a single whole slide image is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology foundation model (FM) that is pre-trained on a diverse dataset of 195 million image tiles collected from multiple sites. We design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks ranging from subcellular- to slide-scale, including instance segmentation, tile classification, and slide-level prediction. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific FMs, some of which use orders-of-magnitude larger datasets and model sizes. Our findings present a path towards a universal embedding to power pathology image analysis, and motivate further exploration around pathology FMs in terms of data diversity, architectural improvements, sample efficiency, and practical deployability in real-world applications. Workshops: - Accessible and Efficient Foundation Models for Biological Discovery - ICML 2024 Workshop on Foundation Models in the Wild - Machine Learning for Life and Material Science: From Theory to Industry Applications - Publication: https://lnkd.in/ewkaGj3D 2. Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities Mechanistic interpretability has been explored in detail for large language models (LLMs). For the first time, we provide a preliminary investigation with similar interpretability methods for medical imaging. Specifically, we analyze the features from a ViT-Small encoder obtained from a pathology Foundation Model via application to two datasets: one dataset of pathology images, and one dataset of pathology images paired with spatial transcriptomics. We discover an interpretable representation of cell and tissue morphology, staining patterns, and gene expression within the model embedding space. Our work paves the way for further exploration around interpretable feature dimensions and their utility for medical and clinical applications. Workshop: ICML 2024 Workshop on Mechanistic Interpretability Publication: https://lnkd.in/eD8JhrFs #ICML #ICML2024

Yesterday, a team of PathAI scientists led by Nhat Le, John Abel, Sean Grullon, and Dinkar Juyal published "𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐨𝐧 𝐚 𝐩𝐚𝐭𝐡𝐨𝐥𝐨𝐠𝐲 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥 𝐫𝐞𝐯𝐞𝐚𝐥𝐬 𝐛𝐢𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥𝐥𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐦𝐨𝐝𝐚𝐥𝐢𝐭𝐢𝐞𝐬." This preprint is a continuation of our initiative to construct pathology-centric #foundationmodels and links PLUTO, PathAI’s foundation model, with interpretable aspects of tumor biology. https://lnkd.in/eD8JhrFs Foundation models are gaining traction in #pathology, however, the applicability of foundation models to diverse use cases rests on their ability to capture latent biology without supervised training. 𝐓𝐨 𝐭𝐞𝐬𝐭 𝐭𝐡𝐢𝐬, 𝐰𝐞 𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐭𝐡𝐫𝐞𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐟𝐨𝐫 𝐥𝐢𝐧𝐤𝐢𝐧𝐠 𝐏𝐋𝐔𝐓𝐎 𝐰𝐢𝐭𝐡 #biology: 1. Relating PLUTO embeddings to spatial #transcriptomics across multiple cancer types. 2. Relating PLUTO embeddings to cell type and morphology. 3. Interrogating PLUTO’s embeddings with a sparse autoencoder–revealing that PLUTO embeddings encode interpretable aspects of the WSI beyond what is typically analyzed in digital pathology. Most excitingly (to us!) we found that deconstructing PLUTO embeddings with the sparse autoencoder revealed unexpected structure in the embeddings. This structure captured subtle aspects of patches from whole slide images (WSIs) including cell morphological subtypes, tissue geometry and collagen alignment, and even tissue preparation characteristics such as small amounts of surgical ink–shown in the figure below. Taken together, these results ground PLUTO in fundamental tumor biology and improve our confidence that PLUTO’s embeddings are biologically interpretable, powerful, and general for downstream tech applications. You can read the preprint here: https://lnkd.in/eD8JhrFs #CancerResearch #Biotech #MachineLearning #DeepLearning #AI #FoundationModels #Pathology #SpatialBiology

Our Chief Medical Officer, Eric Walk MD, FCAP, presented on behalf of PathAI at the #AWSSummit this week in NYC. Dr. Walk shared insights into how PathAI is leveraging cloud infrastructure to unlock the next wave of #precisionmedicine innovation. Using examples from our cutting-edge AISight® image management system and platform, he highlighted the transformative power of #digitalpathology and #AI. At PathAI, we believe the future of patient care will be revolutionized by digital pathology and artificial intelligence, driving better patient outcomes, enhanced diagnostic accuracy, and operational efficiency. Check out some highlights from his presentation and see how PathAI is leading the way in AI-driven and digital diagnostics. Link https://lnkd.in/eR8dWEWu #PathAI #AWSsummit #PrecisionMedicine #DigitalPathology #AI #HealthcareInnovation #Diagnostics #Healthcare #Pathology AISight® is for research use only. Not for use in diagnostic procedures.

🔎 Interested in analyzing the tissue microenvironment in inflammatory bowel disease? View #IBD #pathology samples through the enhanced lens of #AI with IBD Explore™! You can now access our new IBD Explore™ demo to see how #AI-powered digital pathology can supercharge your #digestivedisease research. IBD Explore™ enables a detailed quantification of tissue regions, cellular composition, and inflammatory infiltration in H&E-stained #UC and #CD biopsies. Human-interpretable features from IBD Explore™ can be used to explore tissue composition, quantify treatment effect and mechanism of action, or identify possible #biomarkers predictive of drug response. Sign up for an account to access our demo; if you've already registered an account, the new slides are already available! https://lnkd.in/eCn4xwj2 #pathology #IBD #AI #digitalpathology #precisionmedicine IBD Explore™ is for research use only. Not for use in diagnostic procedures.

🗞 We're thrilled to announce the launch of our new quarterly newsletter, designed to keep you at the forefront of AI-powered #pathology advancements. Stay up to date with the latest PathAI #digitalpathology innovations, announcements, publications, and more. Whether you're a pathologist, researcher, or simply passionate about the future of #medicine, our newsletter is your gateway to understanding how AI is revolutionizing pathology. Sign up: https://lnkd.in/ezFEqmeK #AI #biotech #MachineLearning #DeepLearning #Oncology #biotechnology