|
混合生成的判别式核肾细胞癌的组织学分类
Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma |
|
| |
| 课程网址: |
http://videolectures.net/simbad2011_ulas_carcinoma/
|
| 主讲教师: |
Aydın Ulaş
|
| 开课单位: |
维罗纳大学
|
| 开课时间: |
2011-10-17
|
| 课程语种: |
英语
|
| 中文简介: |
本文建议在肾细胞癌组织微阵列图像中, 采用具有形状特征的先进分类技术进行细胞核分类。我们的目标是提高区分健康细胞和癌细胞的分类准确性。该方法受到自然语言处理的启发: 从自动分割的原子核中提取多个特征, 并将其量化为视觉词, 并将其共同出现编码为视觉主题。为此, 从量化形状描述符 (视觉词) 中学习了一个生成模型--概率潜在语义分析 (plsa)。最后, 我们从学习模型中提取了一个生成分数, 作为新分类器的输入, 定义了一种混合的泛型判别分类算法。我们将结果与功能集中相同的分类器进行比较, 以评估在应用 plsa 时精度的提高。我们证明, 使用 plsa 创建的功能空间比原始特征空间具有更好的精度。
|
| 课程简介: |
In this paper, we propose to use advanced classification techniques with shape features for nuclei classification in tissue microarray images of renal cell carcinoma. Our aim is to improve the classification accuracy in distinguishing between healthy and cancerous cells. The approach is inspired by natural language processing: several features are extracted from the automatically segmented nuclei and quantized to visual words, and their co-occurrences are encoded as visual topics. To this end, a generative model, the probabilistic Latent Semantic Analysis (pLSA) is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input for new classifiers, defining a hybrid generative-discriminative classification algorithm. We compare our results with the same classifiers on the feature set to assess the increase of accuracy when we apply pLSA. We demonstrate that the feature space created using pLSA achieves better accuracies than the original feature space.
|
| 关 键 词: |
肾细胞癌组织微阵列图像形状特征核; 正常细胞; 癌细胞
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2021-09-20：zyk
|
| 阅读次数: |
39
|
|
图 书 馆