【论文笔记】Detection of Glands and Villi by Collaboration of Domain Knowledge and Deep Learning
I Abstract 结构扭曲的腺体和绒毛是慢性炎症的指征。 然而，这两种结构的“二元性”性质导致它们在H＆E组织学组织图像中的检测存在许多模糊性，尤其是当多个实例聚集在一起时。 基于观察得到，一旦检测到这样的对象，可以大大减少检测到的对象附近的模糊性，我们建议在单一框架中结合深度学习和领域知识，同时检测（密切相关） H＆E组织学组织图像中的腺体和绒毛。 我们的方
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相关文章
- 1. 论文笔记之Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
- 2. Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca D
- 3. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 4. 【论文笔记】（一）Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
- 5. 【文献学习】Power of Deep Learning for Channel Estimation and Signal Detection in OFDM
- 6. （转）Nuts and Bolts of Applying Deep Learning
- 7. Bottom-up Object Detection by Grouping Extreme and Center Points 论文笔记
- 8. GoodPoint: unsupervised learning of keypoint detection and description∗
- 9. 《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》笔记
- 10. 论文阅读《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》