MSc.

Imant Daunhawer

PhD Student

E-Mail
dimant@inf.ethz.ch
Phone
+41 44 633 80 71
Address
Department of Computer Science
CAB E 66
Universitätstr. 6
CH – 8092 Zurich, Switzerland
Room
CAB E 66

My broad research interests are representation learning, multimodal learning, generative models, and their applications in medicine.

I completed my Master's in Social and Economic Data Analysis at the University of Konstanz (Germany) in 2018. Besides my main research interests, I enjoy collaborating on interdisciplinary, application-driven research projects.

 

Abstract

Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhibit a gap in generative quality compared to unimodal VAEs, which are completely unsupervised. In an attempt to explain this gap, we uncover a fundamental limitation that applies to a large family of mixture-based multimodal VAEs. We prove that the sub-sampling of modalities enforces an undesirable upper bound on the multimodal ELBO and thereby limits the generative quality of the respective models. Empirically, we showcase the generative quality gap on both synthetic and real data and present the tradeoffs between different variants of multimodal VAEs. We find that none of the existing approaches fulfills all desired criteria of an effective multimodal generative model when applied on more complex datasets than those used in previous benchmarks. In summary, we identify, formalize, and validate fundamental limitations of VAE-based approaches for modeling weakly-supervised data and discuss implications for real-world applications.

Authors

Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt

Submitted

The Tenth International Conference on Learning Representations, ICLR 2022

Date

27.04.2022

Link

Abstract

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (+/-8.8%) and an area-under-the-precision-recall-curve of 28.42% (+/-11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.

Authors

Martin Stocker, Imant Daunhawer, Wendy van Herk, Salhab el Helou, Sourabh Dutta, Frank A. B. A.Schuerman, Rita K. van den Tooren-de Groot, ; Jantien W. Wieringa, Jan Janota, Laura H. van der Meer-Kappelle, Rob Moonen, Sintha D. Sie, Esther de Vries, Albertine E. Donker, Urs Zimmerman, Luregn J. Schlapbach, Amerik C. de Mol, Angelique Hoffmann-Haringsma, Madan Roy, Maren Tomaske, René F. Kornelisse, Juliette van Gijsel, Frans B. Plötz, Sven Wellmann, Niek B Achten, Dirk Lehnick, Annemarie M. C. van Rossum, Julia E. Vogt

Submitted

The Pediatric Infectious Disease Journal, 2022

Date

09.09.2021

LinkDOI

Abstract

Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approximation functions lead to a trade-off between the semantic coherence and the ability to learn the joint data distribution. We propose a new, generalized ELBO formulation for multimodal data that overcomes these limitations. The new objective encompasses two previous methods as special cases and combines their benefits without compromises. In extensive experiments, we demonstrate the advantage of the proposed method compared to state-of-the-art models in self-supervised, generative learning tasks.

Authors

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Submitted

Ninth International Conference on Learning Representations, ICLR 2021

Date

04.05.2021

Link

Abstract

In the quest for efficient and robust learning methods, combining unsupervised state representation learning and reinforcement learning (RL) could offer advantages for scaling RL algorithms by providing the models with a useful inductive bias. For achieving this, an encoder is trained in an unsupervised manner with two state representation methods, a variational autoencoder and a contrastive estimator. The learned features are then fed to the actor-critic RL algorithm Proximal Policy Optimization (PPO) to learn a policy for playing Open AI’s car racing environment. Hence, such procedure permits to decouple state representations from RL-controllers. For the integration of RL with unsupervised learning, we explore various designs for variational autoencoders and contrastive learning. The proposed method is compared to a deep network trained directly on pixel inputs with PPO. The results show that the proposed method performs slightly worse than directly learning from pixel inputs; however, it has a more stable learning curve, a substantial reduction of the buffer size, and requires optimizing 88% fewer parameters. These results indicate that the use of pre-trained state representations hasseveral benefits for solving RL tasks.

Authors

Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco Wiering

Submitted

ICAART 2021

Date

04.02.2021

Link

Abstract

Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. However, existing generative models that approximate a multimodal ELBO rely on difficult or inefficient training schemes to learn a joint distribution and the dependencies between modalities. In this work, we propose a novel, efficient objective function that utilizes the Jensen-Shannon divergence for multiple distributions. It simultaneously approximates the unimodal and joint multimodal posteriors directly via a dynamic prior. In addition, we theoretically prove that the new multimodal JS-divergence (mmJSD) objective optimizes an ELBO. In extensive experiments, we demonstrate the advantage of the proposed mmJSD model compared to previous work in unsupervised, generative learning tasks.

Authors

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Submitted

NeurIPS 2019

Date

22.10.2020

Link

Abstract

PET/CT imaging is the gold standard for the diagnosis and staging of lung cancer. However, especially in healthcare systems with limited resources, costly PET/CT images are often not readily available. Conventional machine learning models either process CT or PET/CT images but not both. Models designed for PET/CT images are hence restricted by the number of PET images, such that they are unable to additionally leverage CT-only data. In this work, we apply the concept of visual soft attention to efficiently learn a model for lung cancer segmentation from only a small fraction of PET/CT scans and a larger pool of CT-only scans. We show that our model is capable of jointly processing PET/CT as well as CT-only images, which performs on par with the respective baselines whether or not PET images are available at test time. We then demonstrate that the model learns efficiently from only a few PET/CT scans in a setting where mostly CT-only data is available, unlike conventional models.

Authors

Varaha Karthik Pattisapu, Imant Daunhawer, Thomas Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt

Submitted

GCPR 2020

Date

12.10.2020

Link

Abstract

Multimodal generative models learn a joint distribution over multiple modalities and thus have the potential to learn richer representations than unimodal models. However, current approaches are either inefficient in dealing with more than two modalities or fail to capture both modality-specific and shared variations. We introduce a new multimodal generative model that integrates both modality-specific and shared factors and aggregates shared information across any subset of modalities efficiently. Our method partitions the latent space into disjoint subspaces for modality-specific and shared factors and learns to disentangle these in a purely self-supervised manner. In extensive experiments, we show improvements in representation learning and generative performance compared to previous methods and showcase the disentanglement capabilities.

Authors

Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt

Submitted

GCPR 2020

Date

10.09.2020

Link

Abstract

Clinical pharmacology is a multi-disciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics and risk factors. Since the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Authors

Gilbert Koch, Marc Pfister, Imant Daunhawer, Melanie Wilbaux, Sven Wellmann, Julia E. Vogt

Submitted

Clinical Pharmacology & Therapeutics, 2020

Date

11.01.2020

LinkDOI

Abstract

Learning from different data types is a long standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. Existing generative models that try to approximate a multimodal ELBO rely on difficult training schemes to handle the intermodality dependencies, as well as the approximation of the joint representation in case of missing data. In this work, we propose an ELBO for multimodal data which learns the unimodal and joint multimodal posterior approximation functions directly via a dynamic prior. We show that this ELBO is directly derived from a variational inference setting for multiple data types, resulting in a divergence term which is the Jensen-Shannon divergence for multiple distributions. We compare the proposed multimodal JS-divergence (mmJSD) model to state-of-the-art methods and show promising results using our model in unsupervised, generative learning using a multimodal VAE on two different datasets.

Authors

Thomas Sutter, Imant Daunhawer, Julia E. Vogt

Submitted

Visually Grounded Interaction and Language Workshop, NeurIPS 2019

Date

12.12.2019

Abstract

Multimodal generative models learn a joint distribution of data from different modalities---a task which arguably benefits from the disentanglement of modality-specific and modality-invariant information. We propose a factorized latent variable model that learns named disentanglement on multimodal data without additional supervision. We demonstrate the disentanglement capabilities on simulated data, and show that disentangled representations can improve the conditional generation of missing modalities without sacrificing unconditional generation.

Authors

Imant Daunhawer, Thomas Sutter, Julia E. Vogt

Submitted

Bayesian Deep Learning Workshop, NeurIPS 2019

Date

12.12.2019

Abstract

Background Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital. Methods We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment. Results Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application. Conclusion Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.

Authors

Imant Daunhawer, Severin Kasser, Gilbert Koch, Lea Sieber, Hatice Cakal, Janina Tütsch, Marc Pfister, Sven Wellman, Julia E. Vogt

Submitted

Pediatric Research, 2019

Date

30.03.2019

LinkDOI