#### Idea
Reverse Classification Accuracy (RCA) models are aims to answer the question on how to estimate performance of models (semantic segmentation models were explained in the paper) in cases where ground truth is not available. 

#### Why is it important

Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. RCA aims to quantify the performance in those deployment scenarios

#### Methodology
The RCA model pipeline follows a simple enough pipeline for the same:

1. Train a model M on training dataset T containing input images  and ground truth  {**I**,**G**}
2. Use M to predict segmentation map for an input image II to get segmentation map SS
3. Train a RCA model that uses input image II to predict SS. As it's a single datapoint for the model it would overfit. There's no validation set for the RCA model
4. Test the performance of RCA model on Images which have ground truth G and the best performance of the model is an indicator of the performance (DSC - Dice Similarity Coefficient)  of how the original image would perform on a new image whose ground truth is not available to compute segmentation accuracy (DSC)

#### Observation
For validation of the RCA method, the predicted DSC and the real DSC were compared and the correlation between the 2 was calculated. For all calculations 3  types of methods of segmentation were used and 3  slightly different types methods for RCA were used for comparison. The predicted DSC and real DSC were highly correlated for most of the cases.

Here's a snap of the results that they obtained

![](http://i.imgur.com/2ra0wQm.png)