11 citations · 37 across the 21 of their papers we have counts for
5 papers · 1 filter
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Matan Atad, Vitalii Dmytrenko, Yitong Li +6
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works…
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang +5
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced. Training…
Interpretable Vertebral Fracture Diagnosis
Paul Engstler, Matthias Keicher, David Schinz +11
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also lea…
Do Explanations Explain? Model Knows Best
Ashkan Khakzar, Pedram Khorsandi, Rozhin Nobahari +1
It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the…
FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays
Matthias Keicher, Kamilia Zaripova, Tobias Czempiel +3
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task. However, the clinical accuracy of free-text reports has prov…