Summary of Panoptic Segmentation Of Mammograms with Text-to-image Diffusion Model, by Kun Zhao et al.
Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model
by Kun Zhao, Jakub Prokop, Javier Montalt Tordera, Sadegh Mohammadi
First submitted to arxiv on: 19 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 proposes a novel approach to breast lesion segmentation in mammography images using vision-language diffusion models. The authors leverage pre-trained features from a Stable Diffusion model as inputs to a state-of-the-art panoptic segmentation architecture, achieving accurate delineation of individual breast lesions. To bridge the gap between natural and medical imaging domains, the authors incorporate a mammography-specific MAM-E diffusion model and BiomedCLIP image and text encoders into the framework. The approach is evaluated on two recently published mammography datasets, CDD-CESM and VinDr-Mammo, achieving competitive performance for both instance and semantic segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make breast cancer diagnosis more accurate by using special computer models to analyze mammography images. These models can help doctors identify individual breast tumors more easily, which is important because there are so many mammograms to review each day. The researchers use a new type of model that combines image and text information to get better results. They test their approach on two sets of mammography images and show it can accurately segment out individual breast lesions. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Semantic segmentation
