  Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of
intratumoral heterogeneity in the study of renal cancer. ccRCC originates from
the epithelial lining of proximal convoluted renal tubules. These cells undergo
abnormal mutations in the presence of Ki67 protein and create a lump-like
structure through cell proliferation. Manual counting of tumor cells in the
tissue-affected sections is one of the strongest prognostic markers for renal
cancer. However, this procedure is time-consuming and also prone to
subjectivity. These assessments are based on the physical cell appearance and
suffer wide intra-observer variations. Therefore, better cell nucleus detection
and counting techniques can be an important biomarker for the assessment of
tumor cell proliferation in routine pathological investigations. In this paper,
we introduce a deep learning-based detection model for cell classification on
IHC stained histology images. These images are classified into binary classes
to find the presence of Ki67 protein in cancer-affected nucleus regions. Our
model maps the multi-scale pyramid features and saliency information from local
bounded regions and predicts the bounding box coordinates through regression.
Our method validates the impact of Ki67 expression across a cohort of four
hundred histology images treated with localized ccRCC and compares our results
with the existing state-of-the-art nucleus detection methods. The precision and
recall scores of the proposed method are computed and compared on the clinical
data sets. The experimental results demonstrate that our model improves the F1
score up to 86.3% and an average area under the Precision-Recall curve as
85.73%.
