Summary of A Novel Momentum-based Deep Learning Techniques For Medical Image Classification and Segmentation, by Koushik Biswas et al.
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
by Koushik Biswas, Ridal Pal, Shaswat Patel, Debesh Jha, Meghana Karri, Amit Reza, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci
First submitted to arxiv on: 11 Aug 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 proposed deep learning-based approach integrates momentum within residual blocks for enhanced training dynamics in medical image analysis, allowing for accurate organ segmentation from CT and MRI scans. This technique outperforms state-of-the-art methods on publicly available benchmarking datasets, such as lung segmentation datasets where it achieves a 5.72% increase in dice score, 5.04% improvement in mean Intersection over Union (mIoU), 8.02% improvement in recall, and 4.42% improvement in precision compared to TransNetR model. The approach also shows promising results in classifying abdominal pelvic CT and MRI scans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to analyze medical images like X-rays and MRIs. They used computer algorithms to help identify different organs and diagnose diseases. Their method was better than other approaches at identifying lungs, livers, and colons from the images. This is important for doctors to make accurate diagnoses and develop treatment plans. |
Keywords
» Artificial intelligence » Deep learning » Precision » Recall
