Summary of Deep Learning For Automated Detection Of Breast Cancer in Deep Ultraviolet Fluorescence Images with Diffusion Probabilistic Model, by Sepehr Salem Ghahfarokhi et al.
Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model
by Sepehr Salem Ghahfarokhi, Tyrell To, Julie Jorns, Tina Yen, Bing Yu, Dong Hye Ye
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper applies a diffusion probabilistic model (DPM) to generate high-quality images for improving breast cancer classification in medical images. Specifically, the authors augment the deep ultraviolet fluorescence (DUV) image dataset with DPM-generated images to enhance intraoperative margin assessment. The proposed approach involves dividing whole surface DUV images into small patches, extracting convolutional features using pre-trained ResNet, and fusing patch-level decisions with a regional importance map computed by Grad-CAM++ for whole surface-level prediction. Experimental results demonstrate that augmenting the training dataset with DPM-generated images significantly improves breast cancer detection performance in DUV images, increasing accuracy from 93% to 97%, outperforming Affine transformations and ProGAN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer program called a “diffusion model” to generate new medical images. The goal is to help doctors better detect breast cancer during surgery. To do this, the authors add these generated images to a existing dataset of real medical images and use a combination of machine learning algorithms to analyze the patches of the image and make predictions about whether or not there is cancer present. The results show that using these new images improves the accuracy of detection from 93% to 97%, which is better than previous methods. |
Keywords
» Artificial intelligence » Classification » Diffusion » Diffusion model » Machine learning » Probabilistic model » Resnet
