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Segmentation Guided Scoring of Pathological Lesions in Swine Through CNNs
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Segmentation Guided Scoring of Pathological Lesions in Swines Through Convolutional Neural Networks


Our UNet-based model can be found in code/

Our rule-based classifier is available as a single function in code/


Annotation Process

The experts followed a layer-wise annotation strategy. Thus, each anatomical structure is fully annotated even when covered by others:

Layers of the annotation process.

Pleural Lesion Scoring Test Set

Test set with 200 examples (50 for each class) is available with no restrictions HERE

Each example comprises:

  • An RGB image (.jpg) with resolution 1040x780 pixels;
  • A stacked segmentation (.npy) with shape 1040x780xC, where C is the number of segmentation classes

Associated labels for the pleurisy score task are available in the raw_labels.txt file. Examples from the 4 classes are reported:

Class i example (Healthy).

Class ii example (Lesion on Chest Wall 1).

Class iii example (Lesion on Chest Wall 2).

Class iv example (Lesion on both Chest Walls).

Train Set and Pre-Trained Weights

Experts put a lot of their valuable time in the annotation process without direct retribution. As such, we must keep track of the dataset's uses by the community.

Research Purposes

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Economic Purposes

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