Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images.

Authors

Hermoza R, Nascimento JC, Carneiro G

Affiliations

• Australian Institute for Machine Learning, The University of Adelaide, Australia. Electronic address: renato.hermozaaragones@adelaide.edu.au.
• Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Portugal. Electronic address: jan@isr.tecnico.ulisboa.pt.
• Centre for Vision, Speech and Signal Processing (CVSSP), The University of Surrey, UK. Electronic address: g.carneiro@surrey.ac.uk.

Abstract

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.