Search Results for author: Daniel Truhn

Found 17 papers, 11 papers with code

Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers

no code implementations11 May 2023 Firas Khader, Jakob Nikolas Kather, Tianyu Han, Sven Nebelung, Christiane Kuhl, Johannes Stegmaier, Daniel Truhn

However, while the conventional transformer allows for a simultaneous processing of a large set of input tokens, the computational demand scales quadratically with the number of input tokens and thus quadratically with the number of image patches.

Image Classification whole slide images

Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs

no code implementations11 May 2023 Firas Khader, Gustav Müller-Franzes, Tianyu Han, Sven Nebelung, Christiane Kuhl, Johannes Stegmaier, Daniel Truhn

X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation where a CT is only acquired for rough measurements such as determining organ size.

Computed Tomography (CT)

Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation

1 code implementation18 Apr 2023 Gustav Müller-Franzes, Fritz Müller-Franzes, Luisa Huck, Vanessa Raaff, Eva Kemmer, Firas Khader, Soroosh Tayebi Arasteh, Teresa Nolte, Jakob Nikolas Kather, Sven Nebelung, Christiane Kuhl, Daniel Truhn

Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement.

MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

no code implementations14 Apr 2023 Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, Keno K. Bressem

As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields.

Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation

1 code implementation7 Nov 2022 Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baessler, Sebastian Foersch, Johannes Stegmaier, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn

Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0. 91 vs. 0. 95 without vs. with synthetic data).

Computed Tomography (CT) Denoising +3

What Does DALL-E 2 Know About Radiology?

no code implementations27 Sep 2022 Lisa C. Adams, Felix Busch, Daniel Truhn, Marcus R. Makowski, Hugo JWL. Aerts, Keno K. Bressem

Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge.

Zero-Shot Text-to-Image Generation

Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

1 code implementation25 Nov 2020 Tianyu Han, Sven Nebelung, Federico Pedersoli, Markus Zimmermann, Maximilian Schulze-Hagen, Michael Ho, Christoph Haarburger, Fabian Kiessling, Christiane Kuhl, Volkmar Schulz, Daniel Truhn

Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used.

Decision Making

An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

no code implementations23 Apr 2020 Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof, Nils Krämer, Daniel Truhn, Burkhard Gess

Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.


Radiomic Feature Stability Analysis based on Probabilistic Segmentations

no code implementations13 Oct 2019 Christoph Haarburger, Justus Schock, Daniel Truhn, Philippe Weitz, Gustav Mueller-Franzes, Leon Weninger, Dorit Merhof

From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.

feature selection

Cannot find the paper you are looking for? You can Submit a new open access paper.