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Found 7 papers, 2 papers with code
Prototypical Self-Explainable Models Without Re-training
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 the other hand, self-explainable models (SEMs) which are trained directly to provide explanations alongside their predictions.
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks.
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The need for interpretable models has fostered the development of self-explainable classifiers.
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Motivated by this, and the observation that the foreground class (e. g., one organ) is relatively homogeneous, we propose a novel anomaly detection-inspired approach to few-shot medical image segmentation in which we refrain from modeling the background explicitly.
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
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 improve performance by recognizing the source domain of an image rather than the medical pathology.
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems.
Considerations for a PAP Smear Image Analysis System with CNN Features
Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the resulting single cell PAP-smear images.
