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Found 10 papers, 3 papers with code
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
In the first step, CDCT uses a Latent Diffusion Model (LDM) to generate a counterfactual trajectory dataset.
Privacy Meets Explainability: A Comprehensive Impact Benchmark
This work is the first to investigate the impact of private learning techniques on generated explanations for DL-based models.
Explainable Artificial Intelligence (XAI)
Privacy Preserving
+1
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.
ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions
This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions.
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders.
XAI Handbook: Towards a Unified Framework for Explainable AI
The field of explainable AI (XAI) has quickly become a thriving and prolific community.
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis
Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable.
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.
Explaining AI-based Decision Support Systems using Concept Localization Maps
We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA.
