Summary of Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration Of Convolutional Neural Networks and Explainable Ai, by Maryam Ahmed et al.
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
by Maryam Ahmed, Tooba Bibi, Rizwan Ahmed Khan, Sidra Nasir
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for enhanced breast cancer diagnosis using the CBIS-DDSM dataset. The methodology includes data preprocessing, advanced augmentation techniques, and transfer learning using pre-trained networks like VGG-16, Inception-V3, and ResNet. The evaluation of XAI’s effectiveness in interpreting model predictions highlights the importance of quantitatively assessing the alignment between AI-generated explanations and expert annotations using the Hausdorff measure. This approach promotes trustworthiness and ethical fairness in AI-assisted diagnostics, critical for AI-driven decisions. The findings demonstrate the effective collaboration between CNNs and XAI in advancing diagnostic methods, facilitating a seamless integration of advanced AI technologies within clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to help doctors diagnose breast cancer better. The researchers created a new way to use special computer models called Convolutional Neural Networks (CNNs) that can explain their decisions. This helps build trust between the doctors and the computers. They used a big dataset of mammography images, applied some fancy math techniques, and even tested how well the AI explanations matched what human experts thought. The results show that this new approach is promising for improving breast cancer diagnosis. |
Keywords
* Artificial intelligence * Alignment * Resnet * Transfer learning
