no code implementations • 28 Mar 2024 • Peng Tang, Tobias Lasser
Firstly, unlike current methods that usually employ two individual models for for clinical and dermoscopy modalities, we verified that multimodal feature can be learned by sharing the parameters of encoder while leaving the individual modal-specific classifiers.
1 code implementation • 15 Jan 2024 • Patris Valera, Josué Page Vizcaíno, Tobias Lasser
We introduce a sparsity-based background removal method by adapting a neural network (SLNet) from a different microscopy domain.
no code implementations • 7 Dec 2023 • Peng Tang, Xintong Yan, Yang Nan, Xiaobin Hu, Bjoern H Menzee. Sebastian Krammer, Tobias Lasser
Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images.
no code implementations • 30 Jul 2023 • Peng Tang, Zhiqiang Xu, Pengfei Wei, Xiaobin Hu, Peilin Zhao, Xin Cao, Chunlai Zhou, Tobias Lasser
To further alleviate the contingent effect of recursive stacking, i. e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions.
1 code implementation • 28 Jul 2023 • Annika Ries, Tina Dorosti, Johannes Thalhammer, Daniel Sasse, Andreas Sauter, Felix Meurer, Ashley Benne, Tobias Lasser, Franz Pfeiffer, Florian Schaff, Daniela Pfeiffer
Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views.
no code implementations • 4 Jul 2023 • Peng Tang, Yang Nan, Tobias Lasser
However, most methods only focus on designing a better module for multi-modal data fusion; few methods explore utilizing the label correlation between SPC and skin disease for performance improvement.
1 code implementation • 10 Jun 2023 • Josué Page Vizcaíno, Panagiotis Symvoulidis, Zeguan Wang, Jonas Jelten, Paolo Favaro, Edward S. Boyden, Tobias Lasser
Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Philip Haitzer, Thomas Sedlmeyr, Sardi Hyska, Johannes Rueckel, Bastian Sabel, Michael Ingrisch, Tobias Lasser
In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler.
no code implementations • 5 Jun 2023 • Alessandro Wollek, Sardi Hyska, Thomas Sedlmeyr, Philip Haitzer, Johannes Rueckel, Bastian O. Sabel, Michael Ingrisch, Tobias Lasser
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports.
no code implementations • 3 Apr 2023 • Theodor Cheslerean-Boghiu, Melia-Evelina Fleischmann, Theresa Willem, Tobias Lasser
A lot of deep learning (DL) research these days is mainly focused on improving quantitative metrics regardless of other factors.
no code implementations • 16 Mar 2023 • Johannes Thalhammer, Manuel Schultheiss, Tina Dorosti, Tobias Lasser, Franz Pfeiffer, Daniela Pfeiffer, Florian Schaff
Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases.
no code implementations • 13 Mar 2023 • Tina Dorosti, Manuel Schultheiss, Felix Hofmann, Johannes Thalhammer, Luisa Kirchner, Theresa Urban, Franz Pfeiffer, Florian Schaff, Tobias Lasser, Daniela Pfeiffer
Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0. 86 [0. 82, 0. 89].
1 code implementation • 3 Mar 2023 • Alessandro Wollek, Robert Graf, Saša Čečatka, Nicola Fink, Theresa Willem, Bastian O. Sabel, Tobias Lasser
Conclusion: ViTs performed similarly to CNNs in CXR classification, and their attention-based saliency maps were more useful to radiologists and outperformed GradCAM.
1 code implementation • 1 Aug 2022 • Alessandro Wollek, Theresa Willem, Michael Ingrisch, Bastian Sabel, Tobias Lasser
The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0. 999 OOD AUC across the three data sets, surpassing all other OOD detection methods.
no code implementations • 1 Jul 2022 • Theodor Cheslerean-Boghiu, Felix C. Hofmann, Manuel Schultheiß, Franz Pfeiffer, Daniela Pfeiffer, Tobias Lasser
We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones.
no code implementations • 12 Oct 2018 • Andreas Maier, Christopher Syben, Tobias Lasser, Christian Riess
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications.