《An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis》--阅读笔记-MLMI
Authors Xueying Shi, Qi Dou, Cheng Xue, Jing Qin, Hao Chen, Pheng-Ann Heng 港中文王平安组是真的屌啊 Motivation 由于皮肤癌是人类最常见的恶性肿瘤之一，因此自动皮肤病变分析在临床实践中非常重要。现有的方法与深度学习已经取得了显着的紧张，但是，严重依赖于大规模标记数据集。文章目的是使用少量的数据，但是仍然取得SOTA
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相关文章
- 1. 《Cost-Effective Active Learning for Hierarchical Multi-Label Classification》--阅读笔记-IJCAI
- 2. Adversarial learning with multi-scale loss for skin lesion segmentation
- 3. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 4. 阅读论文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 5. 论文阅读笔记 Meta-Learning for Batch Mode Active Learning
- 6. 论文阅读笔记（三十四）：An Analysis of Scale Invariance in Object Detection－SNIP
- 7. 《A sparse annotation strategy based on attention-guided active learning for 3D medic》--阅读笔记-Arxiv
- 8. 论文阅读笔记：Transfer Learning for Deep Sentiment Analysis
- 9. FeUdal Networks for Hierarchical Reinforcement Learning 阅读笔记
- 10. 论文阅读笔记《Active One-shot Learning》