Summary of Enhancing Weakly-supervised Histopathology Image Segmentation with Knowledge Distillation on Mil-based Pseudo-labels, by Yinsheng He et al.
Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels
by Yinsheng He, Xingyu Li, Roger J. Zemp
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 framework for histopathology image segmentation addresses the challenge of accurate tumor segmentation under weakly-supervised conditions with coarse-grained image labels. The approach leverages multiple instance learning (MIL) outputs as pseudo-masks for training, introducing an iterative fusion-knowledge distillation strategy to prevent performance deterioration and noise amplification during knowledge distillation. Experimental results on public histopathology datasets demonstrate the effectiveness of this framework in complementing various MIL-based segmentation methods and achieving state-of-the-art (SOTA) performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to segment tumors in images taken from biopsies, which is important for diagnosing cancer. Most current approaches need lots of labeled data, but labeling these images can be very time-consuming and expensive. This paper explores using weaker labels that only identify the image as containing a tumor or not. The authors develop a new method called iterative fusion-knowledge distillation that helps a student model learn from a teacher model’s results. This approach prevents errors from getting amplified during training. The authors test their method on public datasets and show it can improve performance over other methods. |
Keywords
* Artificial intelligence * Image segmentation * Knowledge distillation * Student model * Supervised * Teacher model
