MGH Capstone
Fighting Cancer with a WebApp
Machine Learning
Introduction
Does the shape of a nodule annotation itself reveal something about malignancy?
Spoilers: The malignancy scores (in order) are 5, 5, 4, 4, 2, 3, 3
SAKE is an annotation framework aimed at making labeling nodules easier for radiologists. At the very heart of this software is the belief that accurate and standardized annotations are very important for precise algorithms. We aim to provide analytics behind this claim by investigating how annotation shapes affect predicted malignancy.
In our analysis, we use convolutional neural networks to detect features about shape and structure in nodules annotations.
Assumptions
We are isolating the effect of annotation shape alone. There are clearly other factors at play when determining malignancy which we do not account for in this network:
 Pixelwise intensity in DICOM. A key part of diagnosis is the intensity and thresholds at which certain feature appear within a proposal region.
 Location within lung. We center the annotation region in a box, which in effect removes any localization information in our input data.
 Covariance between neighboring structures. We also do not include information on how many other vesicles or nodules that are nearby, which can be an indication of malignancy as well.
 Out of image context. This refers to any other context a radiologist might use, such as a patient’s previous medical history (eg. smoking, genetics, etc.)
Data
We use data from the National Cancer Institute (NCI) that includes a 15 malignancy rating on DICOM images. For our training and validation purposes, we chose a subset of 1202 annotations. Please click on this link for more details.
Overview
Our ML contribution consists of two parts:
ML Pipeline
 An ML pipeline implemented in Keras and Theano that allows for live ML predictions of nodule segmentations.
ShapeNet: a shapebased ConvNet for Malignancy Prediction
 A convolutional neural network model (informally called ShapeNet) that generates malignancy scores based on the userinputed segmentation shape.
How should we model malignancy?
 Approach 1: A 5class classification problem. Since we have 5 classes as our target variable, we can possibly just do a classification of each of the respective five classes. However, the clear issue arises that a neural network does not treat each class as ranked — they would be distinct classes. We do not expect the difference between a 4 rating and 5 rating to be the same as the difference between a 1 ranting and a 5 rating, so the scheme would be a weak approach. In other words, this is not a problem akin to discrimination between a cat and dog.
 Approach 2: A binary classification problem: We can alternatively just create two classes. Given five classes, we can split the data into [1,2] ratings and [3,4,5] ratings, or [1,2,3] and [4,5] ratings, assigning each a ‘low’ or ‘high’ risk. We were close to implementing our model this way, especially since binary classification methods have easier evaluation regimes. However, we decided against it for two reasons. First, it was not clear that creating a cutoff between low and high risk would be in any way natural — to say that something of a 2 rating was in a different class to something in a 3 rating would still create some of the issues we described above. Second, we wanted to take advantage of the fact that we are in fact given 5 classes and incorporate the relative risk in a meaningful way in the model.
 Approach 3: A regression problem. This seemed to be the most reasonable approach, with some caveats. First, our data is not continuous, so our predictions would yield some extra loss unless they fell directly on one of the five integers. To remedy this, we normalized our data from 0 to 1, so our new targets were [0.2, 0.4, 0.6, 0.8, 1]. Next, since all our labeled data assumed at least some risk of malignancy, we did not have a ‘0’ class (or a class indicating “no malignancy”). This means even when clicking on a random vesicle that does not have some malignancy, our model tends to still give a nonzero value. Therefore, this means our model does not directly model “risk of malignancy” per se, but rather probability of malignancy given a suspected region).
The input volume is a 10x128x128 binary mask of the segmentation polygon. Below are samples animated in the zdirection.
How do we build the architecture?
We build our CNN with the following architecture:
We perform two convolutions in a row before shrinking the image by half for a total of 8 layers. In our flattened layers, we add a dropout of 50% to allow for generalization. Finally, we use ReLU activation on our final layer to accommodate the fact that we are performing a regression.
While parameter selection and tuning can turn into an infinitely time consuming and complex task, we selected several key metaparamters to focus on.

Layers: Results from AlexNet, VGG16, and VGG19 show that depth of a network has a predominant influence on performance.

Data Augmentation: Previous research has also proven that increasing training data by slightly altering input images improves the network’s ability to generalize.

LastLayer activation function: The last layer’s activation function is responsible for how we ‘collapse’ the flattened layer before it, which affects how the output of the network is fed into our loss function.

Input size: Finally, the size of our input can influence both the memory footprint of our network as well as the amount of data we can feed into the model.
The results of parameter tuning are displayed below (full results in next section):
Key takeaways:
 First, more layers does improve performance. We do not expect 16 or 19 layers to be necessary given the simplicity of our training data, so we start with 4 layers and add two more each time. We see that there is only a marginal improvement when we go from 6 layers to 8 layers. Our algorithm performs reasonably fast (42 samples/second), so we can afford 8 layers. However, if speed does become an issue, we note that we can also get similar performance with a 6 layer network.
 Second, data augmentation sightly improves performance. However, the difference in loss is slight, meaning that data gained from flipping the images only provides a little more information to the network.
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 Third, we confirm our intuition that the last layer activation choice of ReLU is more appropriate than sigmoid. We prefer a linear activation since we care about producing predictions relatively smoothly from 0 to 1. Meanwhile, a sigmoid activation forces values to either 0 or 1, increasing our error.
 Finally, we do see that increasing the number of zaxis slices reduces loss to some extent, as the 20x128x128 input model performs slightly better than the 10x128x128 input model. However, this network takes roughly 1.7 times longer to train, which also has implications for prediction time. Prediction also takes 5 times longer (10 samples/second vs 48 samples/ second). More importantly, for our segmentation tool, it will require us to fill in twice the number of segments, which leads to slower performance. Therefore, we prefer a smaller network since over 95% of our data can be contained within 10x128x128 box, even though it leads to a slightly higher loss.
How do we evaluate this model?
We trained and validated our model using a 70/30 split on 1202 total nodule annotations. We decide to measure loss using mean squared error since this is a regression problem. However, since we are using ReLU, the maximum prediction can be unbounded past 1, so we introduce a slightly modified MSE that clips values greater than 1 to be just 1.
The following results are reported with respect to our validation data only.
Firstlayer Kernels
Loss across Epochs
Finally, to evaluate results, we compare both MSE and residual plots when testing on our validation data.
Residual Plot of ShapeNet and Random Noise
Since an explicit formulation of R^2 not available, we chose a rough proxy by comparing the variance of residuals. In effect, we are measuring the spread of the errors. The variance reported is 0.1373 for random predictions and 0.03677 for our model. Note that the red lines indicate the 95th percentile of values.
Results
We aim to investigate how much affect the shape of an annotation has on the probability of malignancy of a proposed region. Using a CNN optimized for performance and speed, we modeled malignancy based off extracted features of the contours and edges in 3D annotations. Our model produces a mean squared error of 0.036774, which is 3.79 times better than random predictions. In addition, the variance of residuals for our model is 0.036771, which is 3.73 times better than random predictions.
Another way to interpret these results is to look at the mean absolute error of 0.15809, which indicates that on average, the difference between the predicted malignancy probability is about 16% compared to the actual malignancy.
Discussion and Future Work
Our results suggest that seeing shape alone can play a significant role in determining the malignancy of a nodule.
We believe this information can be very useful in helping radiologists validate the semiautomated segmentations produced by SAKE. Our hope is that ShapeNet and the SAKE ML pipeline can serve as a benchmark for future work in assisting highquality annotations. In particular, we believe that incorporating imagewise binary classification can be a useful “doublecheck” mechanism for radiologists.
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