{"id":27051,"date":"2026-03-23T16:03:43","date_gmt":"2026-03-23T20:03:43","guid":{"rendered":"https:\/\/www.insightpartners.com\/?p=27051"},"modified":"2026-03-23T16:03:43","modified_gmt":"2026-03-23T20:03:43","slug":"pictor-labs-leadership-story","status":"publish","type":"post","link":"https:\/\/www.insightpartners.com\/ideas\/pictor-labs-leadership-story\/","title":{"rendered":"How Pictor Labs is turning tissue into data to revolutionize diagnosis"},"content":{"rendered":"<p>Histopathology \u2014 examining tissue under a microscope to diagnose disease \u2014 underpins many important decisions in medicine. It\u2019s how clinicians confirm cancer, assess disease progression, and guide treatment plans. Yet the core technology underpinning it hasn\u2019t fundamentally changed in <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10521663\/\">over a century<\/a>.<\/p>\n<p>When a biopsy is sent to the lab for testing, it\u2019s sliced into paper-thin sections that are dipped into a sequence of chemical dyes. The dyes \u201cstain\u201d the tissue samples to reveal hidden cellular structures, helping doctors determine factors such as cancer type or disease progression.<\/p>\n<p>But this dyeing process can be extremely slow and resource-intensive. Labs often batch samples together, meaning it can take days to get results to doctors, and even longer for those results to move through reporting systems, consultations, and scheduling before patients can be appropriately treated.<\/p>\n<p>Further, conventional staining workflows consume and alter tissue sections, meaning each test can leave less material available for crucial next steps like molecular diagnostics, genetic sequencing, and other precision medicine processes.<\/p>\n<p><a href=\"https:\/\/www.linkedin.com\/in\/yair-rivenson-8443b012\/\">Yair Rivenson<\/a>, cofounder and CEO of digital pathology startup <a href=\"https:\/\/www.insightpartners.com\/ideas\/pictorlabs-raises-30-million-in-series-b-funding-to-accelerate-ai-powered-virtual-staining-technology\/\">Pictor Labs<\/a>, saw this process firsthand while working as a researcher at UCLA in 2019. He and cofounder <a href=\"https:\/\/www.linkedin.com\/in\/aydogan-ozcan-3971264\/\">Aydogan Ozcan<\/a> worked with tissue samples as part of their deep learning microscopy research. It seemed strange to him that, in the era of digital and AI, histopathologists still relied on limited physical tissue samples.<\/p>\n<p>\u201cFor me,\u201d he says, \u201cthe question was immediately, why can&#8217;t we teach a computer to do that? Why do we need the chemistry part?\u201d<\/p>\n<p><iframe loading=\"lazy\" title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/oT93QwTiEpg?si=k6KXGiqYcHLVTFec\" width=\"800\" height=\"450\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<h2>Turning cell stains into software<\/h2>\n<p>Pictor Labs exists to simulate the staining process. \u201cThe information is there,\u201d explains Rivenson. \u201cWe can basically <a href=\"https:\/\/www.pictorlabs.ai\/products\">image the tissue<\/a>, edit it as it is in its native form, and compute that contrast so that the pathologist can study the same exact tissue in the exact same way that they&#8217;re currently doing.\u201d<\/p>\n<p>In practice, the process still involves biopsy, tissue sectioning, and glass slides. But instead of staining tissue sections with physical chemical dyes, Pictor Labs images unstained tissue and uses AI to generate virtual stains from the underlying optical signal.<\/p>\n<blockquote>\n<p style=\"padding-left: 40px;\"><em>\u201cThe standard process today is like how they develop film in these different baths. It&#8217;s not [very] different to that.\u201d<\/em><\/p>\n<\/blockquote>\n<p>Then, using technology powered by AI and deep learning algorithms, Pictor Labs can apply virtual stains to the image without physically staining or altering the tissue. \u201cIn other words, now the pathologist can get multiple \u2018stains\u2019 on the same tissue section,\u201d says Rivenson. \u201cIt\u2019s very quick.\u201d<\/p>\n<p>But speed isn\u2019t the only benefit. Running multiple virtual tests on the same tissue section reduces the need for additional biopsies.<\/p>\n<p>\u201cWith the standard process today, you literally need to cut multiple sections from the same biopsy. At some point, you run out of material,\u201d says Rivenson.<\/p>\n<p>And that\u2019s not just because it\u2019s potentially invasive, he adds. \u201cRe-biopsy will cost you thousands of dollars\u2026The problem with healthcare, from what I&#8217;m seeing right now, is there&#8217;s no big thinking [on behalf of] the patient.&#8221;<\/p>\n<p>Pictor Labs\u2019 process is designed to reduce turnaround time, preserve tissue for downstream analysis, and improve workflow efficiency.<\/p>\n<blockquote>\n<p style=\"padding-left: 40px;\"><em>\u201cUntil we break these molds, I don&#8217;t think things will get much better.\u201d<\/em><\/p>\n<\/blockquote>\n<h2>Scaling beyond the lab<\/h2>\n<p>Pictor Labs launched in <a href=\"https:\/\/www.prnewswire.com\/news-releases\/pictorlabs-launches-with-more-than-18-8-million-to-advance-transformative-virtual-staining-301690960.html\">late 2020<\/a> following seed funding, after emerging from research at UCLA School of Engineering and Medicine. As the company evolved, it refined its approach to focus on software and AI algorithms that could integrate into existing pathology and lab workflows.<\/p>\n<p>\u201cIt&#8217;s not enough to come up with the best technology,\u201d explains Rivenson. \u201cWhat is really necessary is to understand the workflow of each and every customer that you talk to. And I have to tell you, we cannot find the exact workflow [of] any lab that we work with. There\u2019s always some wrinkles, something that makes their process slightly unique.\u201d<\/p>\n<blockquote>\n<p style=\"padding-left: 40px;\"><em>\u201cYou need to make your technology as robust and as seamlessly available [as possible], to become part of the workflow.\u201d<\/em><\/p>\n<\/blockquote>\n<p>As Pictor Labs grew, it established partnerships mostly across the pharmaceutical industry to support the adoption of its technology in research and translational settings.<\/p>\n<p>In 2024, the company announced a <a href=\"https:\/\/www.biospace.com\/press-releases\/pictorlabs-raises-30-million-in-series-b-funding-to-accelerate-ai-powered-virtual-staining-technology\">$30M Series B<\/a> led by Insight Partners, with the intention of accelerating adoption, expanding the team, and investing in innovation. In 2025, the company launched <a href=\"https:\/\/www.pictorlabs.ai\/solutions\">ClearStain<\/a>, an AI-powered virtual staining platform designed to support digital sequencing and molecular diagnostic workflows.<\/p>\n<p>Built on Pictor Labs\u2019 earlier iterations, ClearStain packages its technology into a workflow-ready platform that integrates virtual staining directly into the tissue selection and annotation process, rather than operating as a standalone imaging tool. In evaluations, the virtual images closely matched what pathologists were used to seeing on chemically stained slides, across almost every region reviewed.<\/p>\n<h2>Healthcare\u2019s innovation adoption gap<\/h2>\n<p>For all its promise, Rivenson is realistic about what it takes for new technology to gain traction in healthcare. \u201cI think there is an interesting innovation adoption gap,\u201d he says. \u201cHealthcare is a bit more conservative than other systems\u2026The fact that things need to be regulated and approved by a lot of people makes adoption slower.\u201d<\/p>\n<p>Diagnostic workflows generally sit at the center of patient care, so any changes must pass through layers of regulation, testing, and trust. It\u2019s critical that any new approach is additive, rather than disruptive. \u201cSo the technology, other than being amazing and cool, needs to really embed itself in the workflows.\u201d<\/p>\n<blockquote>\n<p style=\"padding-left: 40px;\"><em>\u201cYou want to make sure that the innovation is solving real, grounded problems.\u201d\u00a0<\/em><\/p>\n<\/blockquote>\n<p>That pragmatism extends to how Pictor Labs thinks about AI. While AI underpins its virtual staining technology, Rivenson is clear that automation comes with great responsibility.<\/p>\n<p>\u201cIf you&#8217;re using AI\u2026you&#8217;re still responsible for the [work],\u201d he says. \u201cEvery company that is using AI will need to be somewhat accountable for that, especially in the healthcare industry.\u201d<\/p>\n<h2>Making precision medicine possible for every patient<\/h2>\n<p>Despite these barriers, the direction of travel is clear for Pictor Labs: By digitizing tissue earlier, the company aims to shift histopathology from a linear process battling scarcity into the foundation for data-driven care.<\/p>\n<p>\u201cOnce our technology is embedded with clinical labs, I think that&#8217;s the most important thing and the biggest success for Pictor Labs,\u201d he says. \u201cIf you could change the whole process around pathology upstream [of] the patient, you can start using smaller biopsies, [run] additional analysis, and have enough material to do precision medicine\u2026for every patient.\u201d<\/p>\n<hr \/>\n<p><em>*Note: Insight Partners has invested in Pictor Labs.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Histopathology \u2014 examining tissue under a microscope to diagnose disease \u2014 underpins many important decisions in medicine. It\u2019s how clinicians confirm cancer, assess disease progression, and guide treatment plans. Yet the core technology underpinning it hasn\u2019t fundamentally changed in over a century. When a biopsy is sent to the lab for testing, it\u2019s sliced into [&hellip;]<\/p>\n","protected":false},"author":36,"featured_media":27054,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21,34,132],"tags":[],"class_list":["post-27051","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article","category-video","category-software-leaders","topic-stories","domain-ai-ml","domain-biotech","domain-healthtech"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.1 (Yoast SEO v23.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How Pictor Labs is turning tissue into data to revolutionize diagnosis | Insight Partners<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.insightpartners.com\/ideas\/pictor-labs-leadership-story\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Pictor Labs is turning tissue into data to revolutionize diagnosis\" \/>\n<meta property=\"og:description\" content=\"Histopathology \u2014 examining tissue under a microscope to diagnose disease \u2014 underpins many important decisions in medicine. It\u2019s how clinicians confirm cancer, assess disease progression, and guide treatment plans. Yet the core technology underpinning it hasn\u2019t fundamentally changed in over a century. When a biopsy is sent to the lab for testing, it\u2019s sliced into [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.insightpartners.com\/ideas\/pictor-labs-leadership-story\/\" \/>\n<meta property=\"og:site_name\" content=\"Insight Partners\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-23T20:03:43+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/02\/JPM-2026-thumbnails-7.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"720\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Cecilia Beard\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insightpartners\" \/>\n<meta name=\"twitter:site\" content=\"@insightpartners\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta 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