Summary of A Holistic Weakly Supervised Approach For Liver Tumor Segmentation with Clinical Knowledge-informed Label Smoothing, by Hairong Wang et al.
A Holistic Weakly Supervised Approach for Liver Tumor Segmentation with Clinical Knowledge-Informed Label Smoothing
by Hairong Wang, Lingchao Mao, Zihan Zhang, Jing Li
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper presents a novel holistic weakly supervised framework for accurate CT-based liver tumor segmentation, which is essential for diagnosis and treatment of liver cancer. The framework integrates clinical knowledge to address the challenges of heterogeneous tumors, imprecise margins, and limited labeled data. The authors propose three key components: a knowledge-informed label smoothing technique to regularize model training, a global and local-view segmentation framework, and customized pre- and post-processing pipelines for each subtask. They evaluate their method on the HCC-TACE-Seg dataset and show that these components complementarily contribute to improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Liver cancer is a major cause of death worldwide. Doctors need to accurately identify tumors in CT scans to diagnose and treat patients. Manually drawing tumor boundaries takes too long, is inconsistent, and highlights the need for automated methods. Deep learning has shown promise, but precise liver tumor segmentation remains challenging due to uneven tumors, unclear edges, and limited data. The paper presents a new approach that uses clinical knowledge to improve accuracy. It combines three techniques: generating smooth labels from clinical data, breaking down the task into simpler steps, and customizing processing for each step. They tested their method on real-world data and showed it performs better than existing methods. |
Keywords
» Artificial intelligence » Deep learning » Supervised
