Signed up since Oct. 6, 2019


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@askalik commented on PR #317: Made casepleauralspaces not nullable

I believe this pull request resolves the issue in the bug
1 month, 1 week ago

@askalik opened a new pull request: #317: Made casepleauralspaces not nullable

per open item #200 made casepleauralspace not nullable. Updated template and model <!--- Provide a general summary of your changes in the Title above --> ## Description <!--- Describe your changes in detail --> changd the casepleauralspace so that is not nullable ## Reference to official issue <!--- If fixing a bug, there should be an existing issue describing it with steps to reproduce --> <!--- Please link to the issue here: --> ## Motivation and Context <!--- Why is this change required? What problem does it solve? --> <!--- If adding a new feature or making improvements not already reflected in an official issue, please reference the relevant sections of the design doc --> ## How Has This Been Tested? <!--- Please describe in detail how you tested your changes. --> <!--- Include details of your testing environment, and the tests you ran to --> <!--- see how your change affects other areas of the code, etc. --> ## Screenshots (if appropriate): ## Metrics (if appropriate): If you submitting a PR for a prediction algorithm (segmentation, identification, or classification) please fill in values for as many as the below statistics as are relevant. *algorithms by metric* metric | relevant algorithms -------|-------------------- [accuracy <sup>1</sup> <sup>2</sup>](https://stats.stackexchange.com/a/231237/143678) | classification, identification [data IO](https://unix.stackexchange.com/questions/55212) | classification, identification, segmentation [Dice coefficient <sup>3</sup>](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) | segmentation [disk space usage](https://www.cyberciti.biz/faq/linux-check-disk-space-command) | classification, identification, segmentation [Hausdorff distance <sup>3</sup>](https://en.wikipedia.org/wiki/Hausdorff_distance) | segmentation [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) | segmentation [Log Loss](http://wiki.fast.ai/index.php/Log_Loss) | classification, identification <sup>4</sup> [memory usage](https://stackoverflow.com/questions/110259) | classification, identification, segmentation [prediction time <sup>2</sup>](https://stackoverflow.com/questions/385408) | classification, identification, segmentation [sensitivity <sup>3</sup>](http://wiki.fast.ai/index.php/Deep_Learning_Glossary#Recall) | segmentation [specificity <sup>3</sup>](http://wiki.fast.ai/index.php/Deep_Learning_Glossary#Specificity) | segmentation [training time <sup>2</sup>](https://stackoverflow.com/questions/385408) | classification, identification, segmentation *notes* 1. Use 5-fold cross validation if there is enough time and computational power available, otherwise use a holdout set 1. This metric may be automatically calculated by the machine learning architecture, e.g., Keras 1. The calculations for these metrics [are available here](https://github.com/concept-to-clinic/concept-to-clinic/blob/master/prediction/src/algorithms/segment/src/evaluate.py) 1. In order to calculate Log Loss for identification, the data needs to be arranged in a way that shows for each pixel, whether or not it is a nodule centriod. Restated, the pixel level labels of 1/0 would correspond to centriod/not-centriod. *metrics by algorithm* algorithm | relevant metrics ---------------|------------------ classification | accuracy, data IO, disk space usage, Log Loss, memory usage, prediction time, training time identification | accuracy, data IO, disk space usage, Log Loss, memory usage, prediction time, training time segmentation | data IO, Dice coefficient, disk space usage, Hausdorff distance, Jaccard index, memory usage, prediction time, sensitivity, specificity, training time When reporting your values, please use a format similar to the following example. algorithm | metric | value -------------|--------|------: segmentation | accuracy | 99.5 segmentation | Jaccard index | 0.5 segmentation | prediction time (s) | 45.3 segmentation | memory usage (MB) | 5.4 ## CLA - [ ] I have signed the CLA; if other committers are in the commit history, they have signed the CLA as well
1 month, 1 week ago