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# C-NMC Challenge
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This is the code release for the paper:
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Prellberg J., Kramer O. (2019) Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks. In: Gupta A., Gupta R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore
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# Masterarbeit: Anomalie-Detektion in Zellbildern zur Anwendung der Leukaemieerkennung
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## Usage
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Dritter Platz der ISBI2019 (Supervised) mit Codeanpassungen bzw. Ergaenzungen.
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Use the script `main_manual.py` to train the model on the dataset. The expected training data layout is described below.
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**Offizieller Release von Prellberg et al.:https://github.com/jprellberg/isbi2019cancer**
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Use the script `submission.py` to apply the trained model to the test data.
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## Data Layout
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The training data during the challenge was released in multiple steps which is why the data layout is a little peculiar.
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```
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data/fold_0/all/*.bmp
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data/fold_0/hem/*.bmp
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data/fold_1/...
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data/fold_2/...
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data/phase2/*.bmp
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data/phase3/*.bmp
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data/phase2.csv
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```
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The `fold_0` to `fold_2` folders contain the training images with two subdirectories for the two classes each. The directories `phase2` and `phase3` are the preliminary test-set and test-set respectively and contain images numbered starting from `1.bmp`. The labels for the preliminary test-set are specified in `phase2.csv` which looks as follows:
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```
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Patient_ID,new_names,labels
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UID_57_29_1_all.bmp,1.bmp,1
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UID_57_22_2_all.bmp,2.bmp,1
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UID_57_31_3_all.bmp,3.bmp,1
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UID_H49_35_1_hem.bmp,4.bmp,0
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```
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Zum trainieren und evaluieren von Modellen: run.ipynb
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Bereits trainierte Modelle: ../results/
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