On the reproducibility of expert-operated and robotic ultrasound acquisitions. - Risto Kojcev; Ashkan Khakzar; Bernhard Fuerst; Oliver Zettinig; Carole Fahkry; Robert DeJong; Jeremy D. Richmon; Russell H. Taylor; Edoardo Sinibaldi; Nassir Navab (Int. J. Comput. Assist. Radiol. Surg., 2017)

Learning Interpretable Features via Adversarially Robust Optimization. - Ashkan Khakzar; Shadi Albarqouni; Nassir Navab (MICCAI (6), 2019)

Correction to: On the reproducibility of expert-operated and robotic ultrasound acquisitions. - Risto Kojcev; Ashkan Khakzar; Bernhard Fuerst; Oliver Zettinig; Carole Fakhry; Robert DeJong; Jeremy Richmon; Russell H. Taylor; Edoardo Sinibaldi; Nassir Navab (Int. J. Comput. Assist. Radiol. Surg., 2019)

Explaining Neural Networks via Perturbing Important Learned Features. - Ashkan Khakzar; Soroosh Baselizadeh; Saurabh Khanduja; Seong Tae Kim 001; Nassir Navab (CoRR, 2019)

Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. - Stefan Denner; Ashkan Khakzar; Moiz Sajid; Mahdi Saleh; Ziga Spiclin; Seong-Tae Kim 001; Nassir Navab (BrainLes@MICCAI (1), 2020)

Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods. - Ashkan Khakzar; Soroosh Baselizadeh; Nassir Navab (CoRR, 2020)

Neural Response Interpretation Through the Lens of Critical Pathways. - Ashkan Khakzar; Soroosh Baselizadeh; Saurabh Khanduja; Christian Rupprecht 001; Seong Tae Kim 001; Nassir Navab (CVPR, 2021)

Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models. - Ashkan Khakzar; Sabrina Musatian; Jonas Buchberger; Icxel Valeriano Quiroz; Nikolaus Pinger; Soroosh Baselizadeh; Seong Tae Kim 001; Nassir Navab (MICCAI (3), 2021)

Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features. - Ashkan Khakzar; Yang Zhang; Wejdene Mansour; Yuezhi Cai; Yawei Li; Yucheng Zhang; Seong Tae Kim 001; Nassir Navab (MICCAI (3), 2021)

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs. - Seong Tae Kim 001; Leili Goli; Magdalini Paschali; Ashkan Khakzar; Matthias Keicher; Tobias Czempiel; Egon Burian; Rickmer Braren; Nassir Navab; Thomas Wendler 001 (MICCAI (7), 2021)

Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information. - Yang Zhang; Ashkan Khakzar; Yawei Li; Azade Farshad; Seong Tae Kim 001; Nassir Navab (NeurIPS, 2021)

Do Explanations Explain? Model Knows Best. - Ashkan Khakzar; Pedram Khorsandi; Rozhin Nobahari; Nassir Navab (CVPR, 2022)

Interpretable Vertebral Fracture Diagnosis. - Paul Engstler; Matthias Keicher; David Schinz; Kristina Mach; Alexandra S. Gersing; Sarah C. Foreman; Sophia S. Goller; Juergen Weissinger; Jon Rischewski; Anna-Sophia Dietrich; Benedikt Wiestler; Jan S. Kirschke; et al. (iMIMIC@MICCAI, 2022)

Few-shot Structured Radiology Report Generation Using Natural Language Prompts. - Matthias Keicher; Kamilia Mullakaeva; Tobias Czempiel; Kristina Mach; Ashkan Khakzar; Nassir Navab (CoRR, 2022)

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. - Ashkan Khakzar; Yawei Li; Yang Zhang; Mirac Sanisoglu; Seong Tae Kim 001; Mina Rezaei; Bernd Bischl; Nassir Navab (CoRR, 2022)

CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN. - Matan Atad; Vitalii Dmytrenko; Yitong Li; Xinyue Zhang; Matthias Keicher; Jan Kirschke; Benedikt Wiestler; Ashkan Khakzar; Nassir Navab (CoRR, 2022)

Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images. - Ario Sadafi; Oleksandra Adonkina; Ashkan Khakzar; Peter Lienemann; Rudolf Matthias Hehr; Daniel Rueckert; Nassir Navab; Carsten Marr (IPMI, 2023)

FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays. - Matthias Keicher; Kamilia Zaripova; Tobias Czempiel; Kristina Mach; Ashkan Khakzar; Nassir Navab (MIDL, 2023)

AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments. - Yang Zhang; Yawei Li; Hannah Brown; Mina Rezaei; Bernd Bischl; Philip H. S. Torr; Ashkan Khakzar; Kenji Kawaguchi (CoRR, 2023)

Rethinking Feature Attribution for Neural Network Explanation. - Ashkan Khakzar (2023)

Latent Guard: A Safety Framework for Text-to-Image Generation. - Runtao Liu; Ashkan Khakzar; Jindong Gu; Qifeng Chen; Philip Torr 001; Fabio Pizzati (ECCV (26), 2024)

Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models. - Arshia Hemmat; Adam Davies; Tom A. Lamb; Jianhao Yuan; Philip Torr 001; Ashkan Khakzar; Francesco Pinto (NeurIPS, 2024)

On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions. - Razieh Rezaei; Alireza Dizaji; Ashkan Khakzar; Anees Kazi; Nassir Navab; Daniel Rueckert (CoRR, 2024)

Learning Visual Prompts for Guiding the Attention of Vision Transformers. - Razieh Rezaei; Masoud Jalili Sabet; Jindong Gu; Daniel Rueckert; Philip Torr 001; Ashkan Khakzar (CoRR, 2024)

The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms. - Adam Davies; Ashkan Khakzar (CoRR, 2024)

Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models. - Michael Lan; Philip Torr 001; Austin Meek; Ashkan Khakzar; David Krueger 001; Fazl Barez (CoRR, 2024)

Effortless Efficiency: Low-Cost Pruning of Diffusion Models. - Yang Zhang; Er Jin; Yanfei Dong; Ashkan Khakzar; Philip Torr 001; Johannes Stegmaier; Kenji Kawaguchi (CoRR, 2024)

SafetyDPO: Scalable Safety Alignment for Text-to-Image Generation. - Runtao Liu; Chen I Chieh; Jindong Gu; Jipeng Zhang; Renjie Pi; Qifeng Chen; Philip Torr 001; Ashkan Khakzar; Fabio Pizzati (CoRR, 2024)

A Survey on Transferability of Adversarial Examples Across Deep Neural Networks. - Jindong Gu; Xiaojun Jia; Pau de Jorge; Wenqian Yu; Xinwei Liu; Avery Ma; Yuan Xun; Anjun Hu; Ashkan Khakzar; Zhijiang Li; Xiaochun Cao; Philip Torr 001 (Trans. Mach. Learn. Res., 2024)

A Dual-Perspective Approach to Evaluating Feature Attribution Methods. - Yawei Li; Yang Zhang; Kenji Kawaguchi; Ashkan Khakzar; Bernd Bischl; Mina Rezaei (Trans. Mach. Learn. Res., 2024)

Mixture of Experts Made Intrinsically Interpretable. - Xingyi Yang; Constantin Venhoff; Ashkan Khakzar; Christian Schröder de Witt; Puneet K. Dokania; Adel Bibi; Philip Torr 001 (CoRR, 2025)

How Visual Representations Map to Language Feature Space in Multimodal LLMs. - Constantin Venhoff; Ashkan Khakzar; Sonia Joseph; Philip Torr 001; Neel Nanda (CoRR, 2025)

Minimalist Concept Erasure in Generative Models. - Yang Zhang; Er Jin; Yanfei Dong; Yixuan Wu; Philip Torr 001; Ashkan Khakzar; Johannes Stegmaier; Kenji Kawaguchi (CoRR, 2025)