Dataset: https://www.med.upenn.edu/cbica/brats2020/data.html
369 brains in total, 300 for training, 35 for validation, 34 for testing
2D image slices training: convert pixel-level brain tumor segmentation to image-level brain tumor binary classification
Only use T1 brain slices
rescale image slice pixel values to range [0,1]
if sum(all pixel values in one mask slice) > 0:
label = 1 # has tumor
else:
label = 0 # no tumor
Testing performance (tumor is positive class, tumor:normal ~= 1:1):
TP: 2620.0
TN: 2281.0
FP: 660.0
FN: 327.0
precision: 0.7987804634518151
recall: 0.8890396712236284
f_measure: 0.8414468267997854
accuracy: 0.8323709097763092
auc_roc: 0.9338790120417066
Data Usage Agreement citations:
[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117
[3] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)
[4] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q
[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF