DeepMind Blog

How a Gemma model helped discover a new potential cancer therapy pathway

Read the original on DeepMind Blog →

We’re launching a new 27 billion parameter foundation model for single-cell analysis built on the Gemma family of open models.

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arXiv Machine Learning
Aug 11

TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification

arXiv:2606. 09898v2 Announce Type: replace Abstract: Cancer treatment involves decisions across multiple clinical outcomes, yet pathway-informed deep learning models are typically evaluated in isolation, making their relative benefits unclear.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy
arXiv AI
Jun 11

Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

arXiv:2606. 12346v1 Announce Type: cross Abstract: Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remains a central challenge in computational pathology.

By Kai Standvoss, Miriam H\"agele, Rosemarie Krupar, Julika Ribbat-Idel, Jennifer Altsch\"uler, Gerrit Erdmann, Hans Pinckaers, Evelyn Ramberger, Madleen Drinkwitz, \'Ad\'am N\'arai, Alexander M\"ollers, Katja Lingelbach, Sebastian Kons, Lukas H\"onig, Recepcan Adig\"uzel, Joana Bai\~ao, Alberto Megina Gonzalo, Marius Teodorescu, Marie-Lisa Eich, Paolo Chetta, Shakil Merchant, Verena Aumiller, Simon Schallenberg, Andrew Norgan, Klaus-Robert M\"uller, Lukas Ruff, Maximilian Alber, Frederick Klauschen