RT Journal Article
T1 A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images.
A1 Parra, Rodrigo
A1 Ojeda, Verena
A1 Vázquez Noguera, José Luis
A1 García Torres, Miguel
A1 Mello Román, Julio César
A1 Villalba Cardozo, Cynthia Emilia
A1 Facon, Jacques
A1 Divina, Federico
A1 Cardozo, Olivia
A1 Castillo, Veronica Elisa
A1 Castro Matto, Ingrid
A2 Universidad Nacional de Asunción - Facultad Politécnica
A2 Universidad Americana (PY)
A2 Universidad Pablo de Olavide (ES)
AB In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians. In this work, we propose a novel methodology to quantify the similarity between the decision rules used by a DL model and an ophthalmologist, based on the assumption that doctors are more likely to trust a prediction that was based on decision rules they can understand. Given an eye fundus image with OT, the proposed methodology compares the segmentation mask of OT lesions labeled by an ophthalmologist with the attribution matrix produced by interpretability methods. Furthermore, an open dataset that includes the eye fundus images and the segmentation masks is shared with the community. The proposal was tested on three different DL architectures. The results suggest that complex models tend to perform worse in terms of likelihood to be trusted while achieving better results in sensitivity and specificity.
YR 2021
FD 2021
LK http://hdl.handle.net/20.500.14066/3791
UL http://hdl.handle.net/20.500.14066/3791
LA eng
NO CONACYT - Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-nov-2024