Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans

TL;DR

This paper introduces a novel multi-task learning framework for chest CT scans that simultaneously performs classification, segmentation, reconstruction, and detection, aiming to improve lesion identification in medical imaging.

Contribution

It is the first to incorporate detection into a multi-task learning framework for chest CT analysis and explores different backbones and loss functions for segmentation.

Findings

Enhanced lesion detection accuracy

Effective multi-task learning reduces data requirements

Flexible architecture with various backbones and loss functions

Abstract

Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.