Visible to the public Biblio

Filters: Author is Perera, Yuvin  [Clear All Filters]
2022-12-01
Abeyagunasekera, Sudil Hasitha Piyath, Perera, Yuvin, Chamara, Kenneth, Kaushalya, Udari, Sumathipala, Prasanna, Senaweera, Oshada.  2022.  LISA : Enhance the explainability of medical images unifying current XAI techniques. 2022 IEEE 7th International conference for Convergence in Technology (I2CT). :1—9.
This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.