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

• URL: http://arxiv.org/abs/2010.04713v3
• Date: Wed, 28 Apr 2021 22:20:20 GMT
• 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
• Authors: Farzin Negahbani, Rasool Sabzi, Bita Pakniyat Jahromi, Fateme Movahedi, Mahsa Kohandel Shirazi, Shayan Majidi, Dena Firouzabadi, and Amirreza Dehghanian
• Abstract summary: We introduce a novel pipeline and a backend, namely PathoNet for Ki-67 immunostained cell detection and classification. Despite facing challenges, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date in the harmonic mean measure.
• Score: 2.4378845585726903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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