5 papers
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 a highly demanded requirement for applications in high-risk areas such as medicine. Vision Transformers have mainly been limited to attention extraction to provid…
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…