ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges

no code implementations • 3 Dec 2023 • Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, Brandon Westover

In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses.

Decision Making EEG

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance

1 code implementation • NeurIPS 2023 • Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne

However, for a given dataset, there may be many models that explain the target outcome equally well; without accounting for all possible explanations, different researchers may arrive at many conflicting yet equally valid conclusions given the same data.

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes

1 code implementation • CVPR 2022 • Jon Donnelly, Alina Jade Barnett, Chaofan Chen

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning.

Fairness Image Classification