DeepAI AI Chat
Log In Sign Up

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

by   Ivo M. Baltruschat, et al.

The increased availability of X-ray image archives (e.g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolutions of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data (patient age, gender and acquisition type) in the classification process. In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we evaluate the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. We observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-50, integrating non-image data yields the best overall results.


AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray

Radiologists usually observe anatomical regions of chest X-ray images as...

End-to-End Deep Diagnosis of X-ray Images

In this work, we present an end-to-end deep learning framework for X-ray...

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never bee...

A Relational-learning Perspective to Multi-label Chest X-ray Classification

Multi-label classification of chest X-ray images is frequently performed...

Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

Real-world application of chest X-ray abnormality classification require...

Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm

In recent years, people from all over the world are suffering from one o...

ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure

Deep learning, especially convolutional neural networks, has triggered a...