Dr.

Ece Özkan Elsen

Postdoc

E-Mail
ece.oezkanelsen@inf.ethz.ch
Phone
+41 44 632 59 65
Address
Department of Computer Science
Universitätstr. 6
CH – 8092 Zurich, Switzerland
Room
CAB E 66

I obtained my PhD degree in December 2018 from ETH Zurich. At the time of my Master's Degree, I worked with image processing and machine learning methods using probabilistic models for efficient semantic segmentation and object detection. During my PhD, I have tackled problems of image reconstruction and image processing for cancer research. The goal of my doctoral thesis was to explore methods to characterize bio-mechanical properties of soft tissues using ultrasound where I designed and developed robust computational and image reconstruction techniques for imaging bio-mechanical tissue parametrizations (such as speed-of-sound) using convex optimization methods and inverse problem formulations.

In March 2021, I joined the Medical Data Science group led by Prof. Dr. Julia Vogt at ETH as a Postdoctoral Researcher. I am also the co-leader of the Network of Women in Computer Science (CSNOW).

I am interested in probabilistic models, deep learning and image processing to tackle medical problems and interpret medical data.

You can find a video portrait of me here.

 

Abstract

Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making in healthcare. Fair machine learning has focused on mitigating such biases against disadvantaged or marginalised groups, mainly concentrating on tabular data or natural images. This work presents two novel intra-processing techniques based on fine-tuning and pruning an already-trained neural network. These methods are simple yet effective and can be readily applied post hoc in a setting where the protected attribute is unknown during the model development and test time. In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers. To the best of our knowledge, this is one of the first efforts studying debiasing methods on chest radiographs. Our results suggest that the considered approaches successfully mitigate biases in fully connected and convolutional neural networks offering stable performance under various settings. The discussed methods can help achieve group fairness of deep medical image classifiers when deploying them in domains with different fairness considerations and constraints.

Authors

Ricards Marcinkevics, Ece Özkan Elsen, Julia E. Vogt

Submitted

The Seventh Machine Learning for Healthcare Conference, MLHC 2022

Date

05.08.2022

LinkCode

Abstract

Arguably, interpretability is one of the guiding principles behind the development of machine-learning-based healthcare decision support tools and computer-aided diagnosis systems. There has been a renewed interest in interpretable classification based on high-level concepts, including, among other model classes, the re-exploration of concept bottleneck models. By their nature, medical diagnosis, patient management, and monitoring require the assessment of multiple views and modalities to form a holistic representation of the patient's state. For instance, in ultrasound imaging, a region of interest might be registered from multiple views that are informative about different sets of clinically relevant features. Motivated by this, we extend the classical concept bottleneck model to the multiview classification setting by representation fusion across the views. We apply our multiview concept bottleneck model to the dataset of ultrasound images acquired from a cohort of pediatric patients with suspected appendicitis to predict the disease. The results suggest that auxiliary supervision from the concepts and aggregation across multiple views help develop more accurate and interpretable classifiers.

Authors

Ugne Klimiene, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ece Özkan Elsen, Alyssia Paschke, David Niederberger, Sven Wellmann, Christian Knorr, Julia E Vogt

Submitted

Oral spotlight at the 2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH), ICML 2022

Date

23.07.2022

Link

Abstract

Due to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making, recent research has focused on mitigating biases against already disadvantaged or marginalised groups in classification models. From the perspective of classification parity, the two commonest metrics for assessing fairness are statistical parity and equality of opportunity. Current approaches to debiasing in classification either require the knowledge of the protected attribute before or during training or are entirely agnostic to the model class and parameters. This work considers differentiable proxy functions for statistical parity and equality of opportunity and introduces two novel debiasing techniques for neural network classifiers based on fine-tuning and pruning an already-trained network. As opposed to the prior work leveraging adversarial training, the proposed methods are simple yet effective and can be readily applied post hoc. Our experimental results encouragingly suggest that these approaches successfully debias fully connected neural networks trained on tabular data and often outperform model-agnostic post-processing methods.

Authors

Ricards Marcinkevics, Ece Özkan Elsen, Julia E. Vogt

Submitted

Contributed talk at ICLR 2022 Workshop on Socially Responsible Machine Learning

Date

29.04.2022

LinkCode

Authors

Richard Rau, Ece Özkan Elsen, Batu M. Ozturkler, Leila Gastli, Orcun Goksel

Submitted

IEEE International Ultrasonics Symposium (IUS)

Date

11.08.2020

DOI

Authors

Lisa Ruby, Sergio J. Sanabria, Katharina Martini, Konstantin J. Dedes, Denise Vorburger, Ece Özkan Elsen, Thomas Frauenfelder, Orcun Goksel, Marga B. Rominger

Submitted

Investigative Radiology

Date

30.06.2019

DOI

Authors

Alvaro Gomariz, Weiye Li, Ece Özkan Elsen, Christine Tanner, Orcun Goksel

Submitted

International Symposium on Biomedical Imaging (ISBI)

Date

06.02.2019

DOI

Authors

Stefanie Ehrbar, Alexander Jöhl, Michael Kühni, Mirko Meboldt, Ece Özkan Elsen, Christine Tanner, Orcun Goksel, Stephan Klöck, Jan Unkelbach, Matthias Guckenberger, Stephanie Tanadini-Lang

Submitted

Medical Physics

Date

03.01.2019

DOI

Authors

Ece Özkan Elsen, Valery Vishnevsky, Orcun Goksel

Submitted

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Date

03.03.2018

DOI

Authors

Ece Özkan Elsen, Christine Tanner, Matej Kastelic, Oliver Mattausch, Maxim Makhinya, Orcun Goksel

Submitted

International Journal of Computer Assisted Radiology and Surgery

Date

22.03.2017

DOI

Authors

Ece Özkan Elsen, Gemma Roig, Orcun Goksel, Xavier Boix

Submitted

arXiv

Date

27.05.2016

Authors

Firat Ozdemir, Ece Özkan Elsen, Orcun Goksel

Submitted

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Date

27.05.2016

DOI

Authors

Ece Özkan Elsen, Orcun Goksel

Submitted

International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Date

27.05.2015

DOI