Search Results for author:
Found 8 papers, 0 papers with code
Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes
Objective: We propose a new approach for volumetric instance segmentation in X-ray Computed Tomography (CT) data for Non-Destructive Testing (NDT) by combining the Segment Anything Model (SAM) with tile-based Flood Filling Networks (FFN).
Instance Segmentation XXL-CT Challenge of a Historic Airplane
Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing.
Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures
This results in long training periods of such deep networks and the size of the networks is huge.
An annotated instance segmentation XXL-CT data-set from a historic airplane
To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner.
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
MitoDet: Simple and robust mitosis detection
Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions.
Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification
We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis.
Towards a New Science of a Clinical Data Intelligence
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.
