Machine Learning in Oncology Lab

Application and Collaboration Driven Development of Interpretable and Statistically Sound Machine Learning Methods 🔬

Follow for updates about the group 🔥

[Machine Learning in Oncology Lab @mlo_lab]

Part of our group headed to Marburg for the 13th UCT Science Day!

A great kick-off for our early-stage PhD researchers to present their projects, exchange ideas, and connect with collaborators across the field.

[Machine Learning in Oncology Lab @mlo_lab]

Chat with @lukaskuhn77, @gserpep and Alexander this week at CVPR in Denver!

[Machine Learning in Oncology Lab @mlo_lab]

Azza presented “From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty” at the AISTATS Workshop on Calibration for Modern AI where she won the Best Student Paper Award!

Congratulations, Azza! 🎉

[Machine Learning in Oncology Lab @mlo_lab]

Great work led by first author @arberqoku

[Machine Learning in Oncology Lab @mlo_lab]

New preprint: PACMON
A framework for pathway-level interpretation of multimodal perturbation screens, integrating RNA + protein readouts and scaling to atlas-scale datasets.

https://t.co/fiyjjh9aFL

#bioRxiv #multiomics #computationalbiology

[Machine Learning in Oncology Lab @mlo_lab]

Can we trust LLM confidence? 🤖
Excited to share that our paper “From Entropy to Calibrated Uncertainty” is accepted to the #AISTATS2026 Calibration for Modern AI workshop!

In this work, we introduce a Von Neumann entropy-based approach to align model’s confidence with actual accuracy.

📄 Read: https://t.co/sbkhAXUWS7

In collaboration with @walhanassim, @lukasgradient, @BuettnerFlo 🔥🙌🏻

Attending AISTATS? Come find our poster!

[Lukas Kuhn @lukaskuhn77]

GenAI and classic discriminative ML don't have to be mutually exclusive! Our paper "LVLM-Aided Alignment of Task-Specific Vision Models" has been accepted at CVPR 2026. (@CVPR #CVPR2026)

We use LVLMs to fix what small vision models get wrong, no per-instance human feedback needed.

🧵

[Lukas Kuhn @lukaskuhn77]

We got accepted to the Multimodal Learning and Applications Workshop @CVPR! #CVPR2026

[Machine Learning in Oncology Lab @mlo_lab]

Today Florian presented "AI-based modelling of multi-omics data to dissect inter-patient heterogenity" at the 6th Rhein-Main Cancer Retreat!
His talk highlighted how machine learning approaches can transform complex molecular datasets into clinically actionable insights

[Lukas Kuhn @lukaskuhn77]

Accepted at #CVPR2026! 🙌🏻🥳

We use LVLMs as a bidirectional interface between domain experts and small vision models to detect and correct spurious correlations. No fine-grained annotations needed!

Key Idea: Domain experts provide simple class-level descriptions, and an LVLM Critic & Judge pair automatically translates these into instance-wise correction signals using our novel PPEPS-WGM segmentation on model explanations.

Joint work with Alexander Koebler, Ingo Thon, and @BuettnerFlo! 🔥

[Machine Learning in Oncology Lab @mlo_lab]

Beyond the sessions, the retreat was also about connection, with lively conversations and a round of table tennis in the evenings. Overall, it was a wonderful opportunity to come together as a team, strengthen collaboration, and return with renewed focus and a shared perspective.

[Machine Learning in Oncology Lab @mlo_lab]

For our 2026 group retreat, the entire team gathered at Bildungshaus Kloster Schöntal. Set in the beautiful monastery surroundings, the retreat gave us dedicated time to discuss orga, exchange ideas on science, and deepen our understanding of our wide range of research areas.

[Machine Learning in Oncology Lab @mlo_lab]

RT @lukasgradient: We tested Zero-shot chest X-ray classification across 3 benchmarks:

NOVA outperforms MedCLIP-like (InfoNCE) training b…

[Lukas Kuhn @lukaskuhn77]

Introducing NOVA — Non-Contrastive Vision-Language Alignment!

We show you don't need negative sampling, momentum encoders, or stop-gradients to align vision and language.

Just predict text embeddings from image views + one simple regularizer (hint: it's by @ylecun & @randall_balestr).

🧵👇🏼 1/n

[Machine Learning in Oncology Lab @mlo_lab]

We got accepted at TMLR!

What characteristics of the samples consistently alleviate catastrophic forgetting in memory-based online continual learning?

@gserpep, Ben Werner, and @BuettnerFlo investigated this question under an uncertainty lens.

📜: https://t.co/c8O0HFSFgK

[Machine Learning in Oncology Lab @mlo_lab]

We got accepted at #ICLR2025!

@gserpep and @BuettnerFlo formalized a novel scenario for *online* federated continual learning and introduced an effective memory-based baseline that combines uncertainty-aware updates with random replay.

📄Read more: https://t.co/EM6gEEhYwI