Summary of Topoc: Topological Deep Learning For Ovarian and Breast Cancer Diagnosis, by Saba Fatema et al.
TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis
by Saba Fatema, Brighton Nuwagira, Sayoni Chakraborty, Reyhan Gedik, Baris Coskunuzer
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Algebraic Topology (math.AT)
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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 This research paper explores the potential of deep learning methods in enhancing medical diagnostics and treatment planning by improving accuracy, reproducibility, and speed. The primary challenge lies in overcoming the need for vast amounts of labeled data required to train these models. The authors aim to develop effective clinical decision support systems that can reduce clinicians’ workloads and turnaround times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using deep learning to help doctors diagnose and treat cancer more efficiently. Right now, this process takes a lot of time and requires experts to examine tiny samples under a microscope. The researchers want to use AI to make this process faster, more accurate, and easier for doctors to do. But they need a huge amount of data to train the AI models, which is currently the biggest problem. |
Keywords
* Artificial intelligence * Deep learning
