Search Results for author: Karol Gotkowski

Found 6 papers, 2 papers with code

Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation

no code implementations19 Mar 2024 Karol Gotkowski, Carsten Lüth, Paul F. Jäger, Sebastian Ziegler, Lars Krämer, Stefan Denner, Shuhan Xiao, Nico Disch, Klaus H. Maier-Hein, Fabian Isensee

We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.

Benchmarking Segmentation

ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling

1 code implementation30 Jan 2023 Karol Gotkowski, Shuvam Gupta, Jose R. A. Godinho, Camila G. S. Tochtrop, Klaus H. Maier-Hein, Fabian Isensee

Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, uses a border-core representation to enable instance segmentation, and is trained with a large dataset containing particles of numerous different sizes, shapes, and compositions of various materials.

Computed Tomography (CT) Instance Segmentation +2

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation

no code implementations5 Aug 2022 Camila Gonzalez, Karol Gotkowski, Moritz Fuchs, Andreas Bucher, Armin Dadras, Ricarda Fischbach, Isabel Kaltenborn, Anirban Mukhopadhyay

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation.

Hippocampus Lesion Segmentation +3

M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning

no code implementations1 Jul 2020 Karol Gotkowski, Camila Gonzalez, Andreas Bucher, Anirban Mukhopadhyay

M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans.

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