Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations

TL;DR

This paper introduces a new framework for classifying renal cell carcinoma subtypes using mitochondria-focused image analysis, demonstrating that deep learning models achieve high accuracy despite IHC staining variability.

Contribution

It compares flat hand-crafted features with deep CNN features for RCC subtyping, establishing the effectiveness of deep features in this context.

Findings

Deep CNN features outperform flat features in classification accuracy.

The best model achieves 89% cross-validation accuracy.

Mitochondria-based subtyping is feasible with robust image analysis.

Abstract

Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant alterations to mitochondria between subtypes make immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible