Summary of Siamese Content-based Search Engine For a More Transparent Skin and Breast Cancer Diagnosis Through Histological Imaging, by Zahra Tabatabaei et al.
Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
by Zahra Tabatabaei, Adrián Colomer, JAvier Oliver Moll, Valery Naranjo
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 proposed Computer Aid Diagnosis (CAD) digital pathology tool leverages Deep Learning (DL)-based techniques to aid pathologists in decision-making. A novel Content-Based Histopathological Image Retrieval (CBHIR) approach is developed for breast and skin cancer datasets, utilizing a custom-built Siamese network as a feature extractor. The CBHIR methods are evaluated on public Breast and private Skin datasets, demonstrating top K accuracy and outperforming state-of-the-art models like Convolutional Auto Encoder (CAE). The proposed tool can offer transparent and trustworthy CAD support to pathologists. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Computer Aid Diagnosis is a new digital pathology tool that helps doctors make better decisions. It uses special computer learning techniques called Deep Learning to analyze images of breast and skin cancer. A new way to find similar pictures in medical databases was developed, which can help doctors quickly find the most important information. The tool was tested on real data sets and worked well, even better than other methods! This tool can be very helpful for doctors because it’s easy to understand and reliable. |
Keywords
* Artificial intelligence * Deep learning * Encoder * Siamese network
