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μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation
X-ray computed microtomography ({\mu}-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples.
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming
Although the availability of a large amount of data is usually given for granted, there are relevant scenarios where this is not the case; for instance, in the biomedical/healthcare domain, some applications require to build huge datasets of proper images, but the acquisition of such images is often hard for different reasons (e. g., accessibility, costs, pathology-related variability), thus causing limited and usually imbalanced datasets.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner.
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Image-based tracking of medical instruments is an integral part of surgical data science applications.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
