AI plays a crucial role in perception tasks, such as image classification. A straightforward neural network can accurately classify images into different categories, even if the images contain hidden, occluded, or unusual features. This is because the network is able to learn from the data, rather than relying on manually defined filters for feature detection. In contrast, traditional computer vision algorithms require these filters to be defined for each class of interest, which is difficult or impossible to do for all possible features and variations. As a result, neural networks can outperform traditional algorithms in many cases.
Cancer Detection is one of the areas of healthcare where Deep Learning can play a huge role. It is the leading cause of deaths in the world and one in six deaths can be attributed to cancer [1]. One defining feature of cancer is the rapid creation of abnormal cells that grow beyond their usual boundaries, which can then invade adjoining parts of the body and spread to other organs known as metastases are the primary cause of death from cancer. Our focus will be on Breast Cancer which is one of the most common forms of cancer. For a long time now, oncologists depend on the assessment of scans made by pathologists. We propose to make use of AI to help aid the process. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterized cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer.
Our proposition here is to build a Deep Learning model based on Convolutional Neural Networks to help create a model which can classify histopathological slide images of lymph node sections and determine if a certain image has metastatic tissues. This solution is not intended to replace the medical practitioner in making cancer diagnosis, but to aid and supplement their diagnosis. The kind of cancer in focus here is Breast Cancer, one of the most common forms of cancer.
The models trained on non-augmented images converge to a solution at a much faster rate. An approach that was considered but not tested, was to train a model to a high degree of accuracy. Then use a transfer learning approach as the model already can detect cancer in the center of a slide image. The weights could be locked, and additional layers could be added to detect cancerous cells in the augmented images.
Deep-learning algorithms such as CNNs have been designed to perform computer vision tasks. A multitude of filters encoding different pixel intensity patterns convolve across the image to produce two-dimensional activation maps of each filter.The algorithm takes in images as input from the data and outputs the predicted ID label, 1 or 0, for the images. The image ID and the prediction label is used to check the accuracy of the model.
The data is organized such that the CSV files have the image ID and the label to go along with it. When an image is used in training, the predicted label for that image will be compared with the actual label to evaluate its efficiency. This metric will also be applied on the testing set to evaluate its efficiency as well. Another metric we will be using is the Area under ROC curve. It is a plot of TP vs FP (True Positive vs False positive rate) at different threshold values
The project uses the Histopathological Cancer Detection dataset offered on the Kaggle Competion [2]. It is a slightly updated version of the PatchCamelyon (PCam) dataset which is a vast collection of small image patches taken from larger digital pathology scans of breast lymph node sections.
The breakdown is as follows:
- Training: 153k (0.9) images
- Validation: 17k (0.1) images
- Testing: 57.5k images
A positive label on an image indicates that the center 32x32 pixel region of a patch contains at least one pixel of tumor tissue. This helps in creating models that do not use any zero-padding when using Convolutional Neural Networks
We investigated the dataset to see if the sampled data in training and validation and testing are homogeneously arranged, i.e., equal distribution of images with label 1 (cancerous) and label 0 (benign) in all sets of data.
Exploring further we can see about further statistics in the data. One thing to note here is the mean of the data is about 0.405031. This means that the negative and positive samples in the data aren’t split equally.
Representing the same data as an histogram
Now we can selectively sample images from the test set of normal and cancerous images. This gives us more control over what images we want to see. Once again, we can see the center 32x32 portion of the image to see if the image has cancer cells or not.
We believed the best approach was to use augmented slide images. A known accurate CNN model was trained for 10 epochs with the non-augmented images. The trained model was detecting cancer cells with approximately 87% accuracy on non-augmented slide images. Testing the trained model with augmented slide images, the detection accuracy drops to approximately 70% accuracy. The models trained on non-augmented images converge to a solution at a much faster rate.
To have a model that will detect cancerous cells when not centered and from noisier data, we first need to generate noisy images. We took advantage of some of the built-in Keras functionality in Tensorflow. The load image function has built in image augmentation capabilities. The images were augmented randomly with random augmentations being applies. The images were being shifted, rotated, flipped, color and contrast shifted. The augmented images were used to test trained CNN models on regular slide images. There was a significant drop in detection accuracy on the ‘non-clean’ images.
The CNN network was made to detect many early small features in the image. The model had 256 filters for the first two layers. The model is as follows with 2x2 kernals sizes and filters as follows: 256, 256, 128, 1268, 64, 64, 128, 128.
4 x [Conv -> BatchNorm -> Conv -> ReLU -> BatchNorm -> MaxPool -> Dropout]
As can been interpreted from the graphs, the model was still learning and starting to converge. If the model had more time to train it would eventually be detecting with a higher accuracy. We were working on other model concurrently to try and reduce the jitter in the convergence of the model and speed up training time. The first layer has 8 16x16 filters with up to six CNN layers before 4 fully-connected layers. That model trains at 3 minutes/epoch which is a significant improvement and the jitter during training is reduced.
Model Summary Plot Model Area Under the Curve Model Accuracy Model Loss
After training our model on this data, we used it for Breast Cancer Detection. The model we finalized converged well with a good Area under the Curve even with augmented data. The trained model would detect with a 87% accuracy rate for both augmented slide images and non-augmented slide images. After training the model had an AUC on the trained data of 0.9567 and an AUC of 0.9663 on new data. The state-of-the-art [3] currently (DSF-CNN) achieves an AUC around 0.975. The model even after 125 epochs was still working on converging. With more training the model shows the potential of having a better detection accuracy. We achieved similar detection accuracy results as the model we based our solution off of [4]. It is worth noting that our model achieved that accuracy for both augmented and non-augment images, whereas the model we referenced [4], when tested on augment data, the detection accuracy was between 65% and 75%. The range is due to the random generation of augmented data, the more the augmented data the worse the model performs
[1] World Health Organization: Cancer Factsheet – Cancer Deaths factsheet, WHO
[2] Histopathological Cancer Detection Dataset – Kaggle
[3] Papers with code, “Breast Tumour Classification on PCam”,
Leaderboard – Papers with code
[4] Mattison Hineline, “Cancer CNN Detection”, Kaggle, – CNN Cancer Detection
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