Open evancofer opened 6 years ago
The authors present a "computational staining" approach for H&E stained images from The Cancer Genome Atlas. There are a few samples in TCGA that have corresponding pathology images, and the authors use these samples to train a CNN to detect tumor infiltrating lymphocytes (TILs) (n = 5,455 across 13 cancer-types). Increased TILs is associated with increased survival and the spatial context of the TILs is important. The authors use the computational staining approach to describe lymphocyte clustering patterns and to uncover relationships between tumor regions across cancer-types.
The authors train a two pronged CNN on two distinct tasks: 1) Lymphocyte Detection, 2) Necrosis Segmentation. The lymphocyte detection CNN is initialized with weights predetermined by a convolutional autoencoder (CAE) with 13 encoding layers and 3 layers of max pooling. The CAE encodes position, appearance, and morphology of nuclei. The authors show that CAE pretraining improves accuracy. The authors ditch the reconstruction aspect of the CAE and plug the feature representations as the input to the rest of the CNN architecture (an additional 14 conv layers, 3 max pooling, 1 fully connected). The Necrosis Segmentation is the DeconvNet architecture used to predict labeled necrosis pixels. For each CNN the authors also use several data augmentation strategies like rotations and color perturbation.
The most interesting computational aspect of this paper, in my opinion, was the loss function. Basically, the loss function incorporated an expert pathologist's review of the computational staining results. Three pathologists reviewed select outputs and toggled lymphocyte detection thresholds and retrained after updating labels.
Also, the specifically trained CNN outperforms VGG16 (fine tuned after image net pretraining) architectureby only 0.0312 AUROC (0.9544 vs. 0.9232).
The authors show a number of really cool biological insights. To list a few:
It is available, but was actually a bit difficult to find. Navigated here: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=33948919 then to the FAQ, which directed me to the Github page: https://github.com/SBU-BMI/u24_lymphocyte
https://doi.org/10.1016/j.celrep.2018.03.086