@GreatAndrew90
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📣 Excited to share our new ICML 2025 Spotlight article, “Do Multiple Instance Learning Models Transfer?” – addressing a foundational questions for building robust and generalizable MIL models.
Read the article: arxiv.org/pdf/2506.09022
👉Enhanced Performance & Robustness: Pretrained MIL models consistently lead to improved performance even when the pre-training data comes from a different organ, site, disease model than the target task.
👉Aggregation Transfers: Transfer gains come from the MIL aggregation module, not just patch encoders. Resetting attention layers drops performance by 5–8%, showing they encode generalizable pooling logic.
👉Pancancer Generalization: Models pretrained on a more diverse and challenging data (e.g. 108-class pancancer classification task) achieve the stronger overall transfer performance.
👉Robust benefits across patch encoders: Benefits from MIL transfer are consistent across a wide range of patch encoders, from out-of-domain encoders such as ResNet50 pretrained on natural images, to in-domain encoders including Gigapath and UNIv2.
This research highlights supervised pretraining as a highly accessible path to generalizable MIL models, offering a data and compute-efficient route for developing slide level encoders with flexible combination of MIL method and patch encoder.
Congratulations to @Daniel__Shao @GreatAndrew90 @richardjchen and everyone else who contributed.
Stay tuned for an array of pre-trained MIL models ready to transfer to any task! Visit us at @icmlconf.View quoted post on X
⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner.
🧠💡Our approach:
By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST.
Congratulations to our superstar co-leads @Criis_perez99 and @GreatAndrew90, this was an exciting collaboration with @jonliu123 @SizunJ @ABashashati.
Preprint: arxiv.org/pdf/2502.17761
Demo: vortex-demo.github.io
Read the excellent blog from our superstar grad student @Criis_perez99: lnkd.in/eYQzpyPk
Also see our previous work on 3D Computational Pathology from @GreatAndrew90 published in Cell last year: lnkd.in/eBvrVf-3
Stay tuned for more to come.View quoted post on X
www.jhunewsletter.com From AI to Cancer pathology: Andrew Song’s path to AI-enhanced cancer diagnostics Andrew H. Song, a postdoctoral research fellow at Harvard Medical School, presented his work on applying deep learning tools to cancer pathology at a talk titled “Taming Large-Scale Pathology Data... 