Search Results for author: Ashkan Khakzar

Found 20 papers, 11 papers with code

On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions

1 code implementation1 Jan 2024 Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar, Anees Kazi, Nassir Navab, Daniel Rueckert

In this work, we assess attribution methods from a perspective not previously explored in the graph domain: retraining.

Graph Classification

A Dual-Perspective Approach to Evaluating Feature Attribution Methods

no code implementations17 Aug 2023 Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei

We apply these metrics to mainstream attribution methods, offering a novel lens through which to analyze and compare feature attribution methods.

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models

no code implementations4 Apr 2022 Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab

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.

FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays

no code implementations29 Mar 2022 Matthias Keicher, Kamilia Zaripova, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab

The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task.

Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models

1 code implementation4 Apr 2021 Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab

We present our findings using publicly available chest pathologies (CheXpert, NIH ChestX-ray8) and COVID-19 datasets (BrixIA, and COVID-19 chest X-ray segmentation dataset).

COVID-19 Diagnosis

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

1 code implementation12 Mar 2021 Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.

Computed Tomography (CT) COVID-19 Image Segmentation +2

Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods

no code implementations1 Dec 2020 Ashkan Khakzar, Soroosh Baselizadeh, Nassir Navab

In this work, we empirically show that two approaches for handling the gradient information, namely positive aggregation, and positive propagation, break these methods.

Improving Feature Attribution through Input-specific Network Pruning

no code implementations25 Nov 2019 Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab

Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks.

Network Pruning

Learning Interpretable Features via Adversarially Robust Optimization

no code implementations9 May 2019 Ashkan Khakzar, Shadi Albarqouni, Nassir Navab

In this work, we propose a method for improving the feature interpretability of neural network classifiers.

Decision Making

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