TITLE: PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline 

ABSTRACT: 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.
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.
PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline
http://arxiv.org/abs/2010.04713v3