How Digital Tools Improve Pathology Workflows

Processing whole slide images typically requires analyzing 18,000+ tiles and hours of computation. But what if AI could work like a pathologist? The computational bottleneck: Current AI approaches face fundamental inefficiency. Whole slide images are massive gigapixel files divided into thousands of tiles for analysis. Most systems process every tile regardless of diagnostic relevance, averaging 18,000 tiles per slide. This brute-force approach demands enormous resources and creates clinical adoption barriers. Experienced pathologists don't examine every millimeter uniformly. They strategically focus on diagnostically informative regions while quickly scanning normal tissue or artifacts. Peter Neidlinger et al. developed EAGLE (Efficient Approach for Guided Local Examination), mimicking this selective strategy. The system combines two foundation models: CHIEF for identifying regions meriting detailed analysis, and Virchow2 for extracting features from selected areas. Key metrics: - Speed: Processed slides in 2.27 seconds, reducing computation time by 99% - Accuracy: Outperformed state-of-the-art models across 31 tasks spanning four cancer types - Interpretability: Allows pathologists to validate which tiles informed decisions The authors note that "careful tile selection, slide-level encoding, and optimal magnification are pivotal for high accuracy, and combining a lightweight tile encoder for global scanning with a stronger encoder on selected regions confers marked advantage." Practical implications: This efficiency addresses multiple adoption barriers. Reduced computational requirements eliminate dependence on high-performance infrastructure, democratizing access for smaller institutions. The speed enables real-time workflows integrating into existing diagnostic routines rather than separate batch processing. Most importantly, the selective approach provides interpretability - pathologists can examine specific tissue regions influencing AI analysis, supporting validation and trust-building. Broader context: EAGLE represents a shift from computational brute force toward intelligent efficiency in medical AI. Rather than scaling hardware requirements, it scales down computational demands while improving performance. This illustrates how understanding domain expertise can inform more effective AI architectures than purely data-driven approaches. How might similar efficiency-focused approaches change AI implementation in your field? paper: https://lnkd.in/eR_Hj7ip code: https://lnkd.in/eX8wEfy6 #DigitalPathology #MedicalAI #ComputationalPathology #MachineLearning #ClinicalAI #FoundationModels

Yesterday, I had a conversation with a pathologist who shared this insight: “I’d love a digital pathology workflow where AI can handle tasks like counting mitosis. It would free me up to focus on the parts of pathology I’m truly passionate about.” Counting mitotic figures is critical for assessing tumor grades, but it’s also repetitive and time-consuming, a perfect task for AI. Imagine a workflow where technology handles the routine, freeing pathologists to focus on complex diagnostics and groundbreaking discoveries. This isn’t about replacing pathologists. It’s about empowering them: ✅ Efficiency: Automating time-intensive tasks. ✅ Accuracy: Reducing human error in repetitive processes. ✅ Focus: Allowing pathologists to dedicate more time to meaningful, high-impact work. The result? Faster diagnoses, better patient care, and a profession that’s not only efficient but also deeply fulfilling. The future of pathology isn’t just digital, it’s about amplifying human expertise through technology. What mundane tasks in your field would you hand over to AI so you can focus on what excites you? Let’s discuss. #DigitalPathology #AIInHealthcare #Innovation

New level of accuracy and efficiency in digital pathology with a Transformer-based AI model for whole slide image (WSI) classification. Classifying cancer subtypes and gene mutations from WSIs has long suffered from excessive computational cost, redundant data, and poor generalization across clinical settings. 𝗠𝗚-𝗧𝗿𝗮𝗻𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗺𝘂𝗹𝘁𝗶-𝘀𝗰𝗮𝗹𝗲 𝗴𝗿𝗮𝗽𝗵 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸, achieving state-of-the-art performance in both cancer subtyping and gene mutation detection.  1. Reduced patch redundancy by selecting only 512 high-informative patches per WSI from over 30,000 patches, cutting computational load and improving focus on cancerous regions.  2. Modeled fine-grained tissue structure with a dynamic graph module, improving classification accuracy even for subtypes with subtle morphological differences.  3. Fused multi-scale patch features using an information bottleneck principle, increasing model generalization across data from three cancer subtyping datasets and seven gene mutation datasets. I thought the dynamic structure information learning module (SILM) was cool. It constructs a tissue graph of spatially adjacent patches and applies multi-hop GCN layers. This approach is akin to graph-based modeling in social network analysis and bioinformatics where local connectivity drives representation learning. Introducing a learnable adjacency matrix via attention (e.g., Graph Attention Networks) so the model can discover non-local tissue relationships (e.g. immune cell infiltrates or vascular niches) that pure spatial proximity might miss could be beneficial. Here's the awesome work: https://lnkd.in/gwAaxzmw Congrats to Jiangbo Shi, Lufei Tang, Zeyu Gao, Yang Li, Chunbao Wang, and Tieliang Gong! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

Precision in Cancer Pathology: Histopathology slides are crucial in diagnosing solid tumors, and with digitized whole-slide images (WSIs), deep learning is transforming the field by enabling the extraction of key biomarkers for prognosis and treatment response. The STAMP (Solid Tumor Associative Modeling in Pathology) protocol stands out as an efficient, biomarker-agnostic workflow, simplifying the use of WSIs for deep learning predictions. By integrating genetic and clinicopathologic data, STAMP enables accurate biomarker predictions, such as identifying microsatellite instability in colorectal cancer, through a streamlined five-stage process : problem definition, data preprocessing, modeling, evaluation, and clinical translation, enabling efficient deep learning predictions from pathology data for personalized cancer treatment. Note: Its open-source, globally deployed framework fosters collaboration between clinicians and engineers or anyone who interested in this area, making it a practical and accessible tool for advancing personalized cancer treatment. https://lnkd.in/eBU2eqtt

Brain Cancer Has a New Look: How Pathology Is Going Multimodal On a recent Saturday morning, I was called in to review a brain tumor intraoperatively. We didn’t do a frozen section. We haven’t in years. Instead, we reviewed a stimulated Raman spectroscopy image—a laser-based, label-free technique that produces near real-time images from fresh tissue, without freezing or staining. It’s non-destructive and fast enough to guide surgical decision-making. At our institution, we were the first in the U.S. to formally replace frozen sections for brain tumors with this technology. Not as a supplement—as the standard of care. Everyone benefits: • The surgeon gets an answer in minutes. • The pathology team isn’t tied up at the cryostat. • The patient spends less time under anesthesia and gets more diagnostic clarity. This is a glimpse of where pathology is going. Just as radiology evolved from plain films to CT, MRI, and PET, pathology is becoming multimodal. Optical microscopy, ex vivo and in vivo imaging, multispectral visualization, genomics, and AI—all of it expanding how we see, interpret, and diagnose disease. The microscope isn’t going away. But it’s no longer the only tool in the toolbox. Invenio Imaging Robert Louis, MD, FAANS, FCNS #pathology #digitalpathology #neurosurgery #imaging #innovation

Roche Receives First FDA Breakthrough Status for AI-Driven Companion Diagnostic in Lung Cancer >> 🔘 Roche’s VENTANA TROP2 device is the first AI-powered companion diagnostic (CDx) to receive FDA Breakthrough Device Designation for non-small cell lung cancer (NSCLC), combining immunohistochemistry (IHC), digital pathology, and AI to reach new levels of diagnostic precision 🔘 It uses a digital pathology algorithm (developed with AstraZeneca) to analyze whole-slide images and generate a quantitative TROP2 score, helping identify which patients might benefit from treatment 🔘 This AI-enhanced scoring could accelerate access to DATROWAY®, a TROP2-targeted antibody-drug conjugate (ADC) from AstraZeneca and Daiichi Sankyo, for patients with advanced NSCLC lacking actionable genomic alterations 🔘 The device incorporates Quantitative Continuous Scoring (QCS) to independently detect tumor cells and compute a Normalised Membrane Ratio (NMR), determining if a tumor is TROP2-positive 🔘 While AI handles the heavy-lifting, qualified pathologists still play a key role in reviewing staining, image quality, and providing clinical oversight 🔘 This is the first FDA Breakthrough designation granted to a computational pathology-based CDx, pointing to a future where AI and pathologists work hand-in-hand 👇 Source articles plus image credit in comments #DigitalHealth #AI #Pharma