Deep Learning Approach for Renal Cell Carcinoma Detection, Subtyping, And Grading

Proceedings - International Conference on Image Processing, pp. 3861–3867

Abstract

We propose a comprehensive end-to-end pipeline designed for the detection, subtyping, and grading of tumors. Our proposed methodology initiates the generation of a heat map, indicating the severity of the tumor. Subsequently, the identification of the most critical patches is conducted based on the probability scores. These identified patches are then directed to a grade prediction network. A distinctive aspect of our research lies in being the first to explore an end-to-end pipeline for both heat map generation and grading prediction. Our experiments were conducted leveraging the public, The Cancer Genome Atlas (TCGA) repository, focusing specifically on renal cancer. We introduced additional patch-level labels to improve the model performance. The generation of tumor heat maps targeted three primary cancer subtypes: clear cell, papillary, and chromo-phobe. To enhance our approach, we implemented center-loss and introduced a method aimed at refining the quality of patches. The experimental outcomes highlight superior performance compared to state-of-the-art method. This research contributes to the advancement of tumor detection and grading, emphasizing the significance of an integrated approach for heat map generation and grading prediction.

Authors 7

1. Maroof Abdul Aziz Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision,RWTH Aachen University,Germany

2. Fatemeh Javadian Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision,RWTH Aachen University,Germany

3. Sherin Susheel Mathew
   Affiliation as printed

   Genesys AI Labs,Bangalore,India

4. Gopal Avinash Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision,RWTH Aachen University,Germany

5. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision,RWTH Aachen University,Germany

6. Sonit Singh

   UNSW Sydney

   Affiliation as printed

   UNSW Sydney,School of Computer Science and Engineering,Australia

7. Abin Jose Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision,RWTH Aachen University,Germany