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Taming Detection Transformers for Medical Object Detection

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  1. Chapter and Conference Paper

    Abstract: Automated Detection and Quantification of Brain Metastases on Clinical MRI Data using CNNs

    Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. We evaluate the potential of CNNs for automated detection and quantificat...

    Irada Pflüger, Tassilo Wald, Fabian Isensee in Bildverarbeitung für die Medizin 2023 (2023)

  2. Chapter and Conference Paper

    Abstract: Multi-dataset Approach to Medical Image Segmentation

    The medical imaging community generates a wealth of data-sets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices co...

    Constantin Ulrich, Fabian Isensee, Tassilo Wald in Bildverarbeitung für die Medizin 2024 (2024)

  3. Chapter and Conference Paper

    Abstract: RecycleNet

    Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the ...

    Gregor Koehler, Tassilo Wald, Constantin Ulrich in Bildverarbeitung für die Medizin 2024 (2024)

  4. Chapter and Conference Paper

    Abstract: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection using Contrastive Representations

    Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic obstructive pulmonary disease (COPD) is a prime example, being underdiagno...

    Silvia D. Almeida, Carsten T. Lüth in Bildverarbeitung für die Medizin 2024 (2024)