# Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
## 3 Results  
The classification (arthroscope presence in 2D US images) accuracy of the algorithm was on average 88.6% with a standard deviation of 2.4% (range: 85.5-93.5%). Figure 5 shows an example of a true positive, false positive and false negative for the best performing cross-validation fold.  
Table 3 shows the confusion matrices for the best and worst performing cross-validation fold. Both have a low number of false positives: 13 and 8, respectively. The primary performance difference between these folds was due to the number of false negatives obtained (41 vs. 111 2D US images). US volumes 42 and 65, which are present in the test set of both folds, account for 53 out of 70 additional false negatives. The number of false negatives in volume 42 and 65 was 2 and 9 for the best fold, and 30 and 34 for the worst fold, respectively.  
Based on the visual inspection of the CAMs of the best and worst folds by three reviewers, the localization of the arthroscope was correct in 375/375 (100%) and 277/300 (92%) true positive images, respectively. Examples of correct and in-correct localizations of the CAMs are shown in Figure 6a-d, respectively. The creation of each CAM took 0.31 s on average.  
Figure 5: Examples of classification results with their corresponding CAMs from the best performing fold, with a (**a**) true positive, (**b**) false positive and (**c**) false negative case. The arthroscope is indicated with white arrows. The scale applies to all sub-figures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Best Fold**} & \multicolumn{2}{c}{**Prediction**} & \multirow{2}{*}{**Worst Fold**} & \multicolumn{2}{c}{**Prediction**} \\  & **0** & **1** & & **0** & **1** \\ \hline \multirow{2}{*}{Label} & 0 & 403 & 13 & \multirow{2}{*}{Label} & 0 & 403 & 8 \\  & 1 & 41 & 375 & & 1 & 111 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of the best (left) and worst fold (right). The Label is the ground truth class assigned to the image and the Prediction represents the classification result of the algorithm. Further, “0” represents no arthroscope, and “1” represents that an arthroscope is present in the image.  
For three out of the four US volumes, the centroid of the staple segmentation was within the bounding box drawn based on the CAMs. For the remaining US volume, the shortest distance between the bounding box drawn based on the CAMs and the staple segmentation was 0.2 mm. These results fall within the expected 2.5 mm shortest distance as described in the final paragraph of the 'Materials and Methods chapter'.  
Finally, the algorithm was also evaluated on a cadaver US volume in which a classification accuracy of 83% was obtained. Out of the seven 2D images with an arthroscope, six were correctly classified and the localization was correct in 100% of these true positive cases. Out of the 107 2D images without an arthroscope, 17 were classified incorrectly (false positive). Figure 7a shows an example of a correctly classified US image with correct localization, while Figure 7b shows an example of a false positive classification.