Computer-aided diagnosis
(CAD) is able to assist doctors in understanding
medical images, allowing for cancer diagnosis with greater
sensitivity and specicity, which is critical for patients. Demir and
Yener [8] survey CAD pipelines, separating them into preprocessing,
feature extraction, selection, and nally classication. ey
further document the use of logistic regression, decision trees, knearest
neighbour, and neural networks in existing approaches.
Khalvati et al. [18] use an SVM over MRI scan texture features to
detect prostate cancer in patients. e winners of the Camelyon16
challenge [1], for example, detect breast cancer from images of
lymph nodes.
Deep convolutional neural networks (CNN) have proven to perform
well in image classication [14, 20, 30], object detection [27],
and other visual tasks. ey have found great success in medical
imaging applications [17], and are for example able to detect skin
cancer metastases [34], achieving substantially beer sensitivity
performance than human pathologists. ese methods all operate
on two-dimensional images, typically a cross-sectional image of
the aected body part.
In comparison, CT image scans are three-dimensional volumes
and are usually anisotropic. Deep networks have also been shown
to performwell in 3D segmentation [23], and have been successfully
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