no code implementations • 25 Mar 2024 • Md Abdul Kadir, GowthamKrishna Addluri, Daniel Sonntag
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 22 Mar 2024 • Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul, Hans-Jürgen Profitlich, Moritz Wolf, Daniel Sonntag
While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.
no code implementations • 3 Feb 2024 • Duy M. H. Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert
Contrary to prior work, we propose a novel 2D--3D aggregation mechanism based on a differentiable solver for the \emph{Fused Gromov-Wasserstein Barycenter} problem and the use of an efficient online conformer generation method based on distance geometry.
no code implementations • 18 Nov 2023 • Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert
Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging.
1 code implementation • 20 Jul 2023 • Md Abdul Kadir, Hasan Md Tusfiqur Alam, Daniel Sonntag
However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data.
no code implementations • 5 Jul 2023 • Md Abdul Kadir, Gowtham Krishna Addluri, Daniel Sonntag
Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications.
1 code implementation • NeurIPS 2023 • Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep, Tan N. Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
no code implementations • 6 Jun 2023 • Aliki Anagnostopoulou, Mareike Hartmann, Daniel Sonntag
Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited.
no code implementations • 6 Jun 2023 • Aliki Anagnostopoulou, Mareike Hartmann, Daniel Sonntag
Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process.
no code implementations • 24 May 2023 • Hannes Kath, Thiago S. Gouvêa, Daniel Sonntag
The impact of machine learning (ML) in many fields of application is constrained by lack of annotated data.
no code implementations • 24 May 2023 • Hannes Kath, Bengt Lüers, Thiago S. Gouvêa, Daniel Sonntag
Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas.
no code implementations • 3 Apr 2023 • Md Abdul Kadir, Fabrizio Nunnari, Daniel Sonntag
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification.
no code implementations • 24 Jan 2023 • Siting Liang, Mareike Hartmann, Daniel Sonntag
This paper presents our project proposal for extracting biomedical information from German clinical narratives with limited amounts of annotations.
no code implementations • 30 Dec 2022 • Hasan Md Tusfiqur, Duy M. H. Nguyen, Mai T. N. Truong, Triet A. Nguyen, Binh T. Nguyen, Michael Barz, Hans-Juergen Profitlich, Ngoc T. T. Than, Ngan Le, Pengtao Xie, Daniel Sonntag
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment.
no code implementations • 4 Dec 2022 • Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
no code implementations • LNLS (ACL) 2022 • Mareike Hartmann, Daniel Sonntag
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data.
no code implementations • 28 Feb 2022 • Mareike Hartmann, Aliki Anagnostopoulou, Daniel Sonntag
We propose an approach for interactive learning for an image captioning model.
no code implementations • CVPR 2022 • Duy M. H. Nguyen, Roberto Henschel, Bodo Rosenhahn, Daniel Sonntag, Paul Swoboda
Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance in crowded scenes or in wide spaces.
no code implementations • 20 Jul 2021 • Duy M. H. Nguyen, Truong T. N. Mai, Ngoc T. T. Than, Alexander Prange, Daniel Sonntag
This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading.
no code implementations • 20 May 2021 • Hans-Jürgen Profitlich, Daniel Sonntag
We describe our work on information extraction in medical documents written in German, especially detecting negations using an architecture based on the UIMA pipeline.
no code implementations • 4 Apr 2021 • Duy M. H. Nguyen, Thu T. Nguyen, Huong Vu, Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag
Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task.
no code implementations • 18 Feb 2021 • Ellák Somfai, Benjámin Baffy, Kristian Fenech, Changlu Guo, Rita Hosszú, Dorina Korózs, Fabrizio Nunnari, Marcell Pólik, Daniel Sonntag, Attila Ulbert, András Lőrincz
Our goal is to bridge human and machine intelligence in melanoma detection.
no code implementations • 23 Sep 2020 • Duy M. H. Nguyen, Duy M. Nguyen, Huong Vu, Binh T. Nguyen, Fabrizio Nunnari, Daniel Sonntag
Until now, Coronavirus SARS-CoV-2 has caused more than 850, 000 deaths and infected more than 27 million individuals in over 120 countries.
no code implementations • 11 Jul 2020 • Marimuthu Kalimuthu, Fabrizio Nunnari, Daniel Sonntag
The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts.
no code implementations • 19 May 2020 • Daniel Sonntag, Fabrizio Nunnari, Hans-Jürgen Profitlich
However, the main contribution is a diagnostic and decision support system in dermatology for patients and doctors, an interactive deep learning system for differential diagnosis of malignant skin lesions.
no code implementations • 27 Aug 2019 • Michael Barz, Daniel Sonntag
Our contributions are: (1) we generate a QA training corpus starting from 877 answers from the customer care domain of T-Mobile Austria, (2) we implement a state-of-the-art QA pipeline using neural sentence embeddings that encode queries in the same space than the answer index, and (3) we evaluate the QA pipeline and our re-ranking approach using a separately provided test set.
no code implementations • 21 Aug 2019 • Fabrizio Nunnari, Daniel Sonntag
We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification.
1 code implementation • WS 2019 • Marimuthu Kalimuthu, Michael Barz, Daniel Sonntag
We accelerate the fine-tuning process of the generic model to the target domain.
no code implementations • 11 Oct 2018 • Daniel Sonntag
Interactive cognitive assessment tools may be valuable for doctors and therapists to reduce costs and improve quality in healthcare systems.
1 code implementation • 1 Oct 2018 • Alexander Prange, Michael Barz, Daniel Sonntag
In this paper we provide a categorisation and implementation of digital ink features for behaviour characterisation.
no code implementations • 13 Mar 2018 • Jan Zacharias, Michael Barz, Daniel Sonntag
This paper provides an overview of prominent deep learning toolkits and, in particular, reports on recent publications that contributed open source software for implementing tasks that are common in intelligent user interfaces (IUI).
1 code implementation • 5 Sep 2017 • Daniel Sonntag, Michael Barz, Jan Zacharias, Sven Stauden, Vahid Rahmani, Áron Fóthi, András Lőrincz
Fine-tuning of a deep convolutional neural network (CNN) is often desired.
no code implementations • WS 2017 • Alex Prange, er, Margarita Chikobava, Peter Poller, Michael Barz, Daniel Sonntag
We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR).
no code implementations • 17 Nov 2013 • Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang, Alexander Cavallaro, Peter A. Fasching, Andre Reis, Martin Sedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg, Patricia G. Oppelt, Denis Krompass
We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.