98 research outputs found
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated
pathological images, can alleviate the risk of overlooking the diagnosis.
Unfortunately, in medical imaging, most available datasets are
small/fragmented. To tackle this, as a Data Augmentation (DA) method, 3D
conditional Generative Adversarial Networks (GANs) can synthesize desired
realistic/diverse 3D images as additional training data. However, no 3D
conditional GAN-based DA approach exists for general bounding box-based 3D
object detection, while it can locate disease areas with physicians' minimum
annotation cost, unlike rigorous 3D segmentation. Moreover, since lesions vary
in position/size/attenuation, further GAN-based DA performance requires
multiple conditions. Therefore, we propose 3D Multi-Conditional GAN (MCGAN) to
generate realistic/diverse 32 X 32 X 32 nodules placed naturally on lung
Computed Tomography images to boost sensitivity in 3D object detection. Our
MCGAN adopts two discriminators for conditioning: the context discriminator
learns to classify real vs synthetic nodule/surrounding pairs with noise
box-centered surroundings; the nodule discriminator attempts to classify real
vs synthetic nodules with size/attenuation conditions. The results show that 3D
Convolutional Neural Network-based detection can achieve higher sensitivity
under any nodule size/attenuation at fixed False Positive rates and overcome
the medical data paucity with the MCGAN-generated realistic nodules---even
expert physicians fail to distinguish them from the real ones in Visual Turing
Test.Comment: 9 pages, 6 figures, accepted to 3DV 201
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