  Data limitation is a significant challenge in applying deep learning to
medical images. Recently, the diffusion probabilistic model (DPM) has shown the
potential to generate high-quality images by converting Gaussian random noise
into realistic images. In this paper, we apply the DPM to augment the deep
ultraviolet fluorescence (DUV) image dataset with an aim to improve breast
cancer classification for intraoperative margin assessment. For classification,
we divide the whole surface DUV image into small patches and extract
convolutional features for each patch by utilizing the pre-trained ResNet.
Then, we feed them into an XGBoost classifier for patch-level decisions and
then fuse them with a regional importance map computed by Grad-CAM++ for whole
surface-level prediction. Our experimental results show that augmenting the
training dataset with the DPM significantly improves breast cancer detection
performance in DUV images, increasing accuracy from 93% to 97%, compared to
using Affine transformations and ProGAN.
