164 citations · 191 across the 6 of their papers we have counts for
6 papers
Prototypical Self-Explainable Models Without Re-training
Srishti Gautam, Ahcene Boubekki, Marina M. C. Höhne +1
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on…
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcene Boubekki, Stine Hansen +4
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the…
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Stine Hansen, Srishti Gautam, Robert Jenssen +1
Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation model…
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to impr…
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the unde…
Considerations for a PAP Smear Image Analysis System with CNN Features
Srishti Gautam, Harinarayan K. K., Nirmal Jith +3
It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nucle…