论文标题
PathOnet：深度学习有助于评估Ki-67和肿瘤浸润淋巴细胞（TIL）作为乳腺癌的预后因素；一个大数据集和基线
PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline
论文作者
论文摘要
核蛋白KI-67和肿瘤浸润淋巴细胞（TILS）已被引入作为预测肿瘤进展及其治疗反应的预后因素。文献中强调了KI-67指数和TIL在全球女性中最常见的癌症（BC）等异质肿瘤方法中的价值。由于KI-67的不确定和主观性质以及TILS评分，使用机器学习的自动化方法，特别是基于深度学习的方法引起了人们的注意。但是，深度学习方法需要大量的带注释的数据。如果没有BC KI-67染色的细胞检测和进一步注释细胞的公开基准测试，我们将SHIDC-BC-KI-67作为上述目的的数据集提出。我们还引入了一条新型管道和一个后端，即Ki-67免疫染色细胞检测和分类以及同时确定肿瘤内TILS评分的pathOnet。此外，我们表明，尽管面临挑战，但我们提出的后端PathOnet的表现要优于迄今为止提议的谐波平均值措施的最新方法。
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literature. Due to the indeterminable and subjective nature of Ki-67 as well as TILs scoring, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 stained cell detection and further annotated classification of cells, we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and a backend, namely PathoNet for Ki-67 immunostained cell detection and classification and simultaneous determination of intratumoral TILs score. Further, we show that despite facing challenges, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date in the harmonic mean measure.
