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Found 7 papers, 2 papers with code
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.
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.
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
For glaucoma classification we achieved AUC equal to 0. 874 which is 2. 7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA.
Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.
G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD).
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.
