TITLE: Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning 

ABSTRACT: We present three parallel-beam tomographic datasets of 94 apples with
internal defects along with defect label files. The datasets are prepared for
development and testing of data-driven, learning-based image reconstruction,
segmentation and post-processing methods. The three versions are a noiseless
simulation; simulation with added Gaussian noise, and with scattering noise.
The datasets are based on real 3D X-ray CT data and their subsequent volume
reconstructions. The ground truth images, based on the volume reconstructions,
are also available through this project. Apples contain various defects, which
naturally introduce a label bias. We tackle this by formulating the bias as an
optimization problem. In addition, we demonstrate solving this problem with two
methods: a simple heuristic algorithm and through mixed integer quadratic
programming. This ensures the datasets can be split into test, training or
validation subsets with the label bias eliminated. Therefore the datasets can
be used for image reconstruction, segmentation, automatic defect detection, and
testing the effects of (as well as applying new methodologies for removing)
label bias in machine learning.
We present three parallel-beam tomographic datasets of 94 apples with
internal defects along with defect label files. The datasets are prepared for
development and testing of data-driven, learning-based image reconstruction,
segmentation and post-processing methods. The three versions are a noiseless
simulation; simulation with added Gaussian noise, and with scattering noise.
The datasets are based on real 3D X-ray CT data and their subsequent volume
reconstructions. The ground truth images, based on the volume reconstructions,
are also available through this project. Apples contain various defects, which
naturally introduce a label bias. We tackle this by formulating the bias as an
optimization problem. In addition, we demonstrate solving this problem with two
methods: a simple heuristic algorithm and through mixed integer quadratic
programming. This ensures the datasets can be split into test, training or
validation subsets with the label bias eliminated. Therefore the datasets can
be used for image reconstruction, segmentation, automatic defect detection, and
testing the effects of (as well as applying new methodologies for removing)
label bias in machine learning.
Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning
http://arxiv.org/abs/2012.13346v1