3 citations · 4 across the 6 of their papers we have counts for
6 papers
brat: Aligned Multi-View Embeddings for Brain MRI Analysis
Maxime Kayser, Maksim Gridnev, Wanting Wang +6
We present brat (brain report alignment transformer), a multi-view representation learning framework for brain magnetic resonance imaging (MRI) trained on MRIs paired with clinical…
Shh, don't say that! Domain Certification in LLMs
Cornelius Emde, Alasdair Paren, Preetham Arvind +6
Large language models (LLMs) are often deployed to perform constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their…
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
Maxime Kayser, Bayar Menzat, Cornelius Emde +7
The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making th…
Explaining Chest X-ray Pathologies in Natural Language
Maxime Kayser, Cornelius Emde, Oana-Maria Camburu +3
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medic…
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski +4
Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL…
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
Maxime Kayser, Roger D. Soberanis-Mukul, Anna-Maria Zvereva +3
Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection,…