Deep Learning for Chest X-ray Analysis: A Survey (2103.08700v1)

Abstract: Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

• The paper surveys deep learning applications for chest X-ray analysis, categorizing research by task including image prediction, segmentation, and localization.
• It highlights key challenges in the field, such as the need for explainable AI, model generalization, and integrating clinical data.
• The survey emphasizes the critical role of large public datasets like ChestX-ray14 and MIMIC-CXR in driving research forward.

Deep Learning for Chest X-ray Analysis: A Survey

The paper "Deep Learning for Chest X-ray Analysis: A Survey" provides an exhaustive overview of deep learning applications in the context of chest X-ray (CXR) analysis. It categorizes the existing body of work into several key tasks, shedding light on methodologies, datasets, and commercial utilities that have proliferated over recent years. The paper offers insights into how deep learning has been effectively leveraged to enhance the automated analysis of the most ubiquitous form of radiological exams.

Key Areas of Focus

This survey organizes extant research into distinct categories: image-level prediction, segmentation, localization, image generation, domain adaptation, and other ancillary tasks. Through detailed tabulation, the paper enumerates studies across these dimensions, demarcating the specific label, task, and dataset combinations used.

1. Image-level Prediction: Comprising the largest segment, this category encapsulates studies that utilize convolutional neural networks (CNN) and other deep learning architectures for multi-class pathology classification tasks. Notably, the paper documents extensive exploration into diseases such as pneumonia, tuberculosis, and the advent of COVID-19 specific studies, which underlines the sector's responsiveness to emergent public health challenges.
2. Segmentation: The paper articulates segmentation techniques applied to distinguish anatomical structures or pathologies within CXR images, highlighting the dominance of the U-Net architecture. This segmentation aids not only automated diagnosis but also in improving interpretative accuracy by demarcating fine anatomical details.
3. Localization: The localization segment is highlighted through strategies that identify regions of interest, typically using bounding boxes. Techniques such as YOLO or Mask R-CNN have demonstrated utility in detecting anomalies such as nodules or pneumothorax.
4. Image Generation and Transformation: In this domain, approaches like GANs have been applied to generate training data augmentations and synthetic images. Moreover, these generation techniques are instrumental in developing preprocessing methods like bone suppression to improve clinical interpretability.
5. Domain Adaptation: Addressing generalizability, this category focuses on the ability of models to maintain performance across diverse datasets, overcoming model degradation when applied to data from different institutions or equipment.

Datasets and Public Data Utilization

The paper emphasizes the critical role of large, publicly available datasets in propelling the research community forward. It underscores the significant impact of datasets such as ChestX-ray14, MIMIC-CXR, and others, which have facilitated the surge in publications by providing comprehensive labeled images for training and evaluation. The authors highlight considerations essential for rigorous scientific work, such as label accuracy and image quality that must be heeded when utilizing these datasets.

Challenges and Future Directions

The survey identifies several crucial challenges inherent to the field, namely the need for explainable AI to foster clinical trust, the requirement for models that generalize well across diverse patient populations and institutions, and the necessity of shedding light on task relevancy and robustness of deep learning models. Furthermore, it suggests an integration of clinical data beyond the image itself to further enhance system performance, reflecting the real-world complexity faced by radiologists.

Commercialization and Clinical Translation

The survey also presents a synopsis of commercial products available for CXR analysis, offering insights into application areas such as TB detection, lesion localization, and workflow prioritization. The disparity between active research areas and commercial application insinuates potential paths for meaningful clinical integration, aligning research efforts more closely with healthcare needs.

In conclusion, this survey serves as a comprehensive resource for those entering or continuing work in deep learning for CXR, pointing towards emerging needs and potential research trajectories. By addressing technical and practical challenges while leveraging robust datasets, the future of automated CXR analysis holds considerable promise in transforming diagnostic workflows and enhancing radiological practice.