68 citations · 142 across the 9 of their papers we have counts for
8 papers · 1 filter
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
Yiheng Xiong, Luisa Gallée, Daniel Santak Wolf +2
Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship. However, the deployment (targe…
FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI
Luisa Gallée, Yiheng Xiong, Meinrad Beer +1
Densely annotated medical image datasets that capture not only diagnostic labels but also the underlying reasoning behind these diagnoses are scarce. Such reasoning-related annotat…
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images
Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin +4
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in…
Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson +5
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and pr…
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
Luisa Gallée, Catharina Silvia Lisson, Meinrad Beer +1
Explainability is an important requirement for machine learning models in high-risk domains such as medicine. We introduce HierViT, a hierarchical Vision Transformer that combines…
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model
Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson +5
Due to the sensitive nature of medicine, it is particularly important and highly demanded that AI methods are explainable. This need has been recognised and there is great research…
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
Luisa Gallee, Meinrad Beer, Michael Goetz
Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In…
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…