Blog Posts
Read blog posts that discuss our company history, team members, and other industry-relevant topics.
Meet Kwadjo Nyante, EMBA, MSc², CISSP, Chief Information Security Officer at Aignostics!
Kwadjo and the Information Security team are responsible for ensuring all our operations remain secure, while protecting our customers, partners, and sensitive healthcare data from cyber threats and data handling risks.
Towards Robust Foundation Models for Digital Pathology
This comprehensive study evaluated 20 pathology foundation models and determined that all 20 encode medical center information, leading to the potential for systematic diagnostic errors and clinical failures that prevent safe adoption.
Explainable AI for Weakly Supervised Membrane Expression Scoring
Presented at ECDP 2025. This study demonstrates that explainable AI can effectively bridge the gap between interpretable and high-performance approaches in medical image analysis. The approach's generalizability makes it particularly valuable—this xAI technique can be applied as post-processing to diverse medical imaging tasks beyond PD-L1 scoring.
Enhancing Prognostic Precision in Bladder Cancer: AI-Driven Tumor Microenvironment Analysis from H&E Images
Presented at ASCO 2025. Our TME-enhanced model outperformed traditional UICC staging, achieving higher predictive accuracy and clearer separation of prognostic risk groups. This demonstrates that integrating TME data from routine H&E slides with UICC staging improves risk stratification, helping pinpoint high-risk bladder cancer patients more precisely than anatomical staging alone.
Meet PD Dr. med. Julika Ribbat-Idel, IFCAP, Principal Pathologist on our Pathology team!
Julika and the Pathology team serve as the medical backbone within Aignostics. Their primary responsibility is bringing specialized medical expertise to a tech-focused company, making sure Aignostics' AI can accurately analyze tissue samples across various applications.
From Bench to Bedside: Generalizable AI Model for ADC Biomarker Evaluation in NSCLC
Presented at AACR 2025. This study demonstrates the potential of AI models to address key challenges in ADC biomarker evaluation for NSCLC. The strong alignment between our model predictions and pathologist assessments demonstrates the value of our automated scoring approach.
Meet Neelay Shah, Machine Learning Engineer on our Data Processing team!
Neelay and the Data Processing team develop pipelines to process data at scale for use-cases such as training foundation models. They ensure that data and models can be processed in a distributed fashion, efficiently spreading computational work across multiple machines to allow for scaling when large data sets are involved. They also partner closely with the Data Science team who use the infrastructure developed by the Machine Learning Engineers to perform experiments for client projects.
Meet Srishti Munjal Mehta, Scientific Program Manager on our Translational Programs team!
Srishti and the Translational Programs team work closely with clients on translational R&D projects including topics such as comprehensive tissue profiling and spatial analyses, precise quantification of challenging biomarkers, and prediction of biomarker expression.
Pathology's Evolution: How Aignostics Builds on Charité Berlin's 300-Year Legacy
Aignostics started in 2018 within the walls of the Berlin Institute of Health at Charité – Universitätsmedizin Berlin, before ultimately spinning-out in 2020. But did you know that Charité Berlin has a rich history dating all the way back to 1710? Let’s explore some of that history, along with the history of pathology’s digital transformation, to understand the origins and drivers of Aignostics’ founding.
Meet Timo Haschler, Senior Scientific Project Manager on our Target and Biomarker Discovery team!
Timo Haschler and his team work closely with clients to identify novel drug targets and biomarkers. This includes the multi-year collaboration we announced with Bayer in 2024, to identify novel cancer targets and co-create a target identification platform to enable better patient identification, stratification, and selection for clinical trials.
