When a CAD-AI network is created and employed, both safety and effectiveness need to be guaranteed for all subgroups of the target population. We present a novel toolbox for automatic slicing and performance assessment in a generic and modular approach, helping to find the subpopulations where cautiousness is warranted, and the model may need improvement. In a first step slices are generated and saved for further analysis inspired by the existing ‘Slice Finder’ algorithm. Depending on the type of AI task (classification, object detection, segmentation, instance segmentation...) multiple metrics are evaluated. Both labeled (specificity, sensitivity...), unlabeled (outlier score, confidence score...) and user-defined metrics can be included. Optionally, the confidence interval (CI) is calculated. In a last step, the metric values and CI are used to rank the slices to quickly find the slices of interest. Custom ranking methods can be added, keeping the full process from slice generation up to and including visualization modular and customizable. We illustrate the toolbox with a dermatology classification and object detection use-case. First a single model is evaluated down to crosses of three slices where slices of interest are detected on degree three which would be difficult to find if not automated. Additionally, the usage of unlabeled metrics such as outlier score is illustrated to automatically find slices of interest.
Accurate monitoring of leaves and plants is a necessity for research on plant physiology. To aid this biological research,
we propose a new active contour method to track individual leaves in chlorophyll fluorescence time laps sequences. The
proposed active contour algorithm is developed such that it can handle sequences with low temporal resolution. This paper
proposes a novel optimization method which incorporates prior knowledge about the plant shape. Tests show that the
proposed framework outperforms state of the art tracking methods.
Registration of multispectral images remains a challenging task due to the lack of stable feature points. Methods based
on intensities are generally more robust for multi-modal image registration, but are computationally demanding or are
restrictive to the transformation model allowed in the registration. This paper proposes a new registration framework
which overcomes these drawbacks. The proposed method optimizes the location of a set of virtual landmarks in order to
get robust and accurate registration.
This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D
fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual
scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming
from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over
0.98.
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