Updated preprint: Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction arxiv.org/abs/2410.00945

I never feel more European than when on the Eurostar train Köln <--> Brussels and they make the announcements in French, Dutch, German and English (and I progressively go from understanding 0% to 100%).

Updated preprint: Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts arxiv.org/abs/2410.06723 While PFMs provide strong representations, robust deployment remains constrained by the quality of the data used to train downstream models.

New preprint: Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction arxiv.org/abs/2604.24679 We benchmark 12 pathology foundation models for survival prediction from WSIs across three independent cohorts with over 5,400 breast cancer patients.

New preprint: BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence arxiv.org/abs/2604.03216 github.com/SeanWu25/Beh...

New preprint: SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG arxiv.org/abs/2603.09940 github.com/fregu856/Sig...

My year of reading in 2025: www.fregu856.com/post/year_of... I read 113 papers in 2025, complete list: github.com/fregu856/pap... Top 25 papers that I found particularly interesting and/or well written (in alphabetical order):

New preprint, work led together with Erik Thiringer: Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models. arxiv.org/abs/2601.04163

I just reached 500 read papers on the Github repository I use to track and organize my reading: github.com/fregu856/pap...

Very happy to have joined the group of David Clifton at IBME in Oxford as a postdoc, to work on machine learning for healthcare! The group is also recruiting multiple new postdocs, please apply before August 18: eng.ox.ac.uk/jobs/job-det...

Not super happy with my assigned NeurIPS papers this year, I found them less interesting/relevant than I usually do. But oh well, still quite solid papers overall, and I do think it's good to be forced to read papers from slightly different areas sometimes.

Our paper "Taming Diffusion Models for Image Restoration: A Review" has now been published, work led by Ziwei Luo: royalsocietypublishing.org/doi/10.1098/...

New preprint, work lead by Ziwei Luo: Forward-only Diffusion Probabilistic Models. arxiv.org/abs/2505.16733 github.com/Algolzw/FoD algolzw.github.io/fod/

I've been trying to properly understand how/why DINOv2 works, and I think this is a good sequence of papers to read for that: (BYOL) Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (NeurIPS 2020) (DINO) Emerging Properties in Self-Supervised Vision Transformers (ICCV 2021)

I really didn't like the ICML review template though, why do we have to make things overly complicated? Please just give me some variation of Summary, Strenghts, Weaknesses, Questions, Detailed comments and Justification of rating!

Finished my 5 #ICML reviews, and realized that I now have passed 100 reviewed papers in total during my career. Actually feels like a pretty cool milestone!

First time I'm reviewing for MIDL. Quite interetsing papers overall, and I like the review template. But, 8 pages in this template seems too short. Not enough space to actually do things properly (e.g., explain the method in detail ~and~ have an extensive experimental evaluation).

My year of reading in 2024: www.fregu856.com/post/year_of... I read 99 papers in 2024. Complete list: github.com/fregu856/pap... Top 15 favorite papers that I found particularly interesting and/or well-written (in alphabetical order):

Recent preprint: Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction. arxiv.org/abs/2410.00945

Recent preprint: Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts arxiv.org/abs/2410.06723