The remainder of this paper is organized as follows: "Related work" reviews related work on multi-task learning, chest X-ray analysis, and explainable AI. "Preliminaries" presents the preliminaries covering background on chest X-ray disease diagnosis, multi-task learning concepts, dataset details, and problem formulation. "Proposed system" details the proposed CXR-MultiTaskNet architecture, including data preprocessing, model design, loss functions, and explainability modules. "Experimental results" presents the experimental setup, quantitative and qualitative results, and ablation studies. "Discussion" provides a comprehensive discussion and outlines limitations of the study in "Limitations of the study". Finally, "Conclusion and future work" concludes the paper and highlights future research directions to improve further clinical adoption and scalability of automated chest X-ray analysis systems.
Recent advancements in deep learning have significantly improved chest X-ray analysis, particularly in disease localization and classification. This section reviews key contributions across multi-task learning frameworks, transformer-based models, and explainable AI techniques. It further highlights efforts in addressing domain adaptation, data imbalance, and noisy labels, providing a comprehensive background for the proposed CXR-MultiTaskNet framework.
Recent developments in multi-task learning frameworks have further advanced chest X-ray analysis by enabling the joint modeling of disease classification and localization, along with related auxiliary tasks, thereby improving model efficiency and clinical relevance. Mohamed et al. DeepChest presented a model-agnostic multi-task learning (MTL) approach that adaptively assigns task weights, achieving both efficiency and accuracy better than all previous ones. Similarly, Okolo et al. CLN, a dual-branch architecture achieving impressive classification (AUC 0.918) and localization (IoU 0.855) on ChestX-ray14. Dealing with resolution limitations, Akhter et al. To facilitate diagnosis in limited-resource settings, developed the MLCAK method, which learns to transfer knowledge from high-resolution to low-resolution CXRs. Zhu et al. They employed a Multi-task UNet architecture to train a saliency model and a disease classifier jointly, yielding benefits for both interpretability and diagnosis accuracy.
Ai et al. tackled pneumonia diagnosis. Specifically, proposed a multiple-task learning (MTL) framework that integrates patient-specific demographics, such as age, gender, and diabetes duration, as features. Al Zahrani et al. An MTL model focused on detecting thoracic pathologies leveraged the simultaneous understanding of pneumonia and COVID-19, as described in. In addition to these works, Kamiri et al. (A systematic review of MTL in medical imaging identified several potential research gaps, including the combination of multiple tasks into a single one and irregularities in results due to generalizability issues. Lastly, Qiu et al. proposed a cooperative MTL framework, where both radiological gaze prediction and diagnosis were compared in an explainable and clinically visionary manner.
Xu et al. Multi-task transformer model that has been tuned with unified instructions to achieve strong performance in classification, localization, and report generation on a large-scale collection of chest radiographs. Li et al. presented a novel work that constructed the RMT (Robust Multimodal Transformer) framework to jointly leverage X-ray and clinical text data for assessing pneumonia presence in pediatric patients, which is more capable of handling missing modalities and appears to be robust. Ghamizi et al. Improving model generalization across datasets was reported in, which demonstrated that learning auxiliary tasks in MTL frameworks improves cross-domain chest disease classification. Lin et al. With the approach of residual learning and MTL combined, lung nodules detection and classification were enhanced using Res-MTNet.
Zhang et al. CXR-Net -- A neural network for explainable and accurate COVID-19 pneumonia diagnosis, combining classification and explainability modules. Park et al. The authors of present a novel vision and language approach called M4CXR, which incorporates multi-tasking capabilities into a large multimodal language model for chest X-ray interpretation, encompassing both report generation and image captioning for the M4CXR component. Zhu et al. In fine-grained tasks, leveraging a multi-task transformer has been used to label chest X-ray reports, facilitating the automatic extraction of various diagnostic terms. Liao et al. An approach to improving X-ray image analysis through shared representation learning across tasks: MTPRet.
Wang et al. applied a multi-task learning framework to predict central venous catheter status from radiographs, addressing both detection and classification within a single model. Finally, Ullah et al. proposed a dual encoder-decoder deep learning framework for anatomical structure segmentation in chest X-rays, improving feature localization and segmentation accuracy. Tan et al. presented DeepPulmoTB, a benchmark dataset that supports multi-task learning of tuberculosis lesions in lung CT images, facilitating both lesion segmentation and classification. Gende et al. proposed an end-to-end multi-task framework for simultaneous epiretinal membrane segmentation and screening in OCT images, highlighting the broader application of multi-task learning in medical imaging.
Transformer architectures and self-supervised learning have transformed radiograph interpretation by capturing complex dependencies, leveraging unlabeled data, and integrating multimodal clinical information. Liao et al. proposed MUSCLE, a self-supervised continual learning framework that pre-trains models across multiple X-ray datasets, addressing data heterogeneity and enhancing transferability for classification and segmentation. Dong et al. designed a multi-channel MTL framework to predict EGFR and KRAS mutations in lung cancer using CT images, demonstrating the clinical potential of combining imaging and patient data. Xiao et al. explored masked autoencoders for multi-label classification of thoracic diseases, demonstrating improved feature learning and robustness in chest X-ray analysis. Li et al. introduced a visual-semantic embedded knowledge graph to enhance multi-label learning, improving the contextual understanding of radiology images. Huang et al. proposed a two-stage training framework with label decoupling and reconstruction to address the challenges of long-tailed multi-label medical image recognition.
Taslimi et al. developed SwinCheX, a transformer-based architecture for multi-label thoracic disease detection, emphasizing the benefits of hierarchical attention mechanisms. Chen et al. introduced BOMD, a bag-of-multi-label descriptors framework, improving noisy chest X-ray classification through advanced representation learning. Hayat et al. applied generalized zero-shot learning for chest radiograph classification, enabling the recognition of unseen diseases by leveraging semantic relationships. Wang et al. proposed a multi-granularity cross-modal alignment approach for generalizable medical visual representation learning, enhancing the interpretability of chest X-ray analysis. Zhou et al. advanced radiograph representation learning using masked record modeling, which integrates textual clinical records with visual features for improved disease prediction.
Ye et al. introduced continual self-supervised learning for universal multi-modal medical data representation, facilitating cross-domain transfer in chest radiograph analysis. Gündel et al. proposed integrating external knowledge sources to improve chest abnormality classification under noisy labels, enhancing robustness and generalization. Nguyen et al. focused on multi-language radiology report mining, building a deep learning model for thoracic disease diagnosis from Vietnamese chest radiographs. Jang et al. significantly improved zero-shot pathology classification on chest X-rays by fine-tuning image-text encoders, enabling scalable detection of unseen diseases. Seibold et al. introduced a report-guided contrastive training approach that allows chest X-ray models to recognize a flexible range of pathologies by aligning image features with the semantics of clinical reports, thereby moving beyond fixed-label classification.
Gazda et al. proposed a self-supervised CNN framework for chest X-ray classification, significantly reducing annotation requirements while achieving strong diagnostic performance. Zhao et al. presented a segment-enhanced contrastive learning approach to improve medical report generation, facilitating better alignment between image regions and textual descriptions. Vu et al. developed MedAug, a contrastive learning framework that leverages patient metadata to improve chest X-ray representations, thereby enhancing model robustness and transferability. Sîrbu et al. introduced GIT-CXR, a transformer-based end-to-end framework that simultaneously generates chest X-ray diagnostic reports, enhancing clinical interpretability while also facilitating pathology detection. Yu and Zhou optimized a transformer model with multi-scale feature fusion, improving the detection efficiency of thoracic diseases in chest X-rays.
Deep learning-based classification approaches have focused on handling the multi-label nature of chest X-ray datasets, addressing thoracic diseases with models leveraging residual learning, ensemble methods, and domain-specific optimizations. Su et al. explored multi-view MTL for CT image quality assessment, extending MTL beyond classification. Kumar et al. applied ensemble learning with preprocessing and feature optimization for the rapid diagnosis of pulmonary diseases. Alshmrani et al. presented a deep learning model for multi-class lung disease classification using CXR, achieving competitive accuracy across multiple conditions. Kim et al. utilized EfficientNet for end-to-end multi-class lung disease classification, emphasizing the importance of transfer learning in enhancing diagnostic efficiency. Emara et al. combined super-resolution and classification using InceptionResNetv2, improving the quality and accuracy of lung disease detection on low-resolution scans.
Lastly, Shaheed et al. proposed an optimized Xception model with XGBoost for multiclass chest disease classification, improving computational efficiency and diagnostic accuracy in resource-constrained environments. In the pursuit of high-precision lung disease detection, Shamrat et al. introduced MobileLungNetV2, a fine-tuned MobileNetV2 model that achieved remarkable classification accuracy for 14 lung conditions using the ChestX-ray14 dataset. Deepak and Bhat proposed a multi-stage deep learning pipeline for comprehensive lung disease classification, integrating data augmentation and multi-scale learning for improved generalization. Li et al. presented a Multi-Level Residual Feature Fusion Network, effectively enhancing thoracic disease classification through deep residual learning and multi-scale feature extraction. Chowdary and Kanhangad introduced a dual-branch network that integrates global and local features for thoracic disease diagnosis, enhancing multi-label classification performance on chest X-rays.
Malik et al. combined chest X-rays, CT scans, and cough sound images in a deep learning framework, demonstrating a novel multimodal approach for classifying chest diseases. Xu et al. proposed MS-ANet, an automated multi-label detection system using attention-based networks for thoracic disease classification, achieving superior results on multiple datasets. Bhusal and Panday utilized DenseNet for multi-label classification, emphasizing the need for robust feature extraction in detecting multiple thoracic abnormalities. Holste et al. discussed the challenges of long-tailed disease distributions in chest X-rays and introduced the CXR-LT challenge to benchmark models on real-world imbalance scenarios. Lai et al. addressed noisy labels and class imbalance in long-tailed, multi-label classification using advanced label correction and class distribution modeling techniques.
Wang et al. introduced a cross- and intra-image prototypical learning method, enhancing interpretability and multi-label classification accuracy on thoracic images. Kufel et al. applied transfer learning for chest X-ray abnormality classification, showcasing improved generalization through fine-tuned pre-trained models. Öztürk et al. proposed HydraViT, a transformer-based adaptive multi-branch model for thoracic disease classification, outperforming CNN-based models in capturing complex feature dependencies. Efimovich et al. integrated deep learning with natural language processing for multilabel classification of lung diseases, facilitating better alignment between radiological images and textual reports for enhanced clinical interpretability. Ukwuoma et al. designed a hybrid explainable ensemble transformer encoder for pneumonia detection from chest X-rays, combining performance with interpretability.
Explainable AI techniques, such as saliency mapping, attention mechanisms, and anatomically consistent embedding, have been integrated into chest X-ray models to improve interpretability and clinical trust. Elhanashi et al. combined classification and localization tasks using a unified network, demonstrating improved interpretability for multi-type chest X-ray abnormalities. Mochurad et al. applied CNN-based methods for basic chest abnormality classification, emphasizing efficient feature learning from X-ray images. Sajed et al. provided a systematic review comparing deep learning and traditional machine learning for lung disease diagnosis, highlighting the superior performance of deep architectures in recent studies. Azad et al. explored multi-disease detection combining ML and DL techniques, emphasizing lightweight frameworks for resource-limited settings. El Asnaoui et al. reviewed automated pneumonia detection, underscoring the need for better annotated datasets and explainable models.
Liu et al. proposed a deep learning model for differentiating TB and non-tuberculous mycobacterial lung disease (NTM-LD) using chest X-rays, validated through a clinical cross-sectional study. Wang et al. demonstrated the multi-site applicability of AI models in diagnosing chronic obstructive pulmonary disease (COPD) from chest X-rays, emphasizing the role of cross-modal and multi-institutional learning frameworks. Cheng et al. proposed a deep learning-based object detection strategy to simultaneously classify and localize thoracic abnormalities, thereby enhancing interpretability and diagnostic accuracy. Mostafa et al. presented a comprehensive survey of AI techniques for thoracic disease diagnosis, highlighting trends in deep learning, transfer learning, and explainability. Li et al. focused on interpretable thoracic pathology prediction through group-disentangled representation learning, improving transparency in disease classification.
Zhou et al. developed an anatomically consistent embedding framework to enhance radiograph interpretation, aligning feature learning with human anatomical structures for better explainability. Celniak et al. improved thoracic radiograph classification through inter-species and inter-pathology self-supervised pre-training, demonstrating the transferability of learned features across veterinary and human datasets. Tran et al. developed a deep CNN model for diagnosing multiple diseases in pediatric chest radiographs, achieving high accuracy by learning from noisy and incomplete labels. Tiu et al. demonstrated expert-level detection of chest pathologies using self-supervised learning on unannotated X-rays, highlighting the scalability of unsupervised feature learning. Van der Sluijs et al. explored image augmentations in Siamese networks for chest X-ray representation learning, improving disease classification performance with contrastive learning techniques.
Lian et al. proposed a DRD U-Net integrated with WGAN and deep neural networks for lung image segmentation, demonstrating improved boundary delineation in chest CT scans. Xiao and Zhao developed a multi-task conditional GAN for synthesizing 3D liver CT images from vascular structures, illustrating the versatility of generative models in medical imaging. Sharmay et al. explored transfer learning in histopathology through the HistoTransfer framework, providing insights applicable to chest X-ray domain adaptation. Feng et al. applied deep supervised domain adaptation to improve pneumonia diagnosis across chest X-ray datasets with distributional differences. Lastly, Lee and Liu demonstrated a real-time end-to-end deep learning framework for lane detection and path prediction in autonomous driving, illustrating the effectiveness of multi-task learning beyond healthcare.
Studies have addressed critical challenges such as domain shifts, noisy annotations, and imbalanced class distributions, proposing domain adaptation, noise-robust training, and augmentation strategies to enhance generalization. Holste et al. discussed the challenges of long-tailed disease distributions in chest X-rays and introduced the CXR-LT challenge to benchmark models on real-world imbalance scenarios. Lai et al. addressed noisy labels and class imbalance in long-tailed, multi-label classification using advanced label correction and class distribution modeling techniques. Wang et al. introduced a cross- and intra-image prototypical learning method, enhancing interpretability and multi-label classification accuracy on thoracic images. Kufel et al. applied transfer learning for chest X-ray abnormality classification, showcasing improved generalization through fine-tuned pre-trained models. Öztürk et al. proposed HydraViT, a transformer-based adaptive multi-branch model for thoracic disease classification, outperforming CNN-based models in capturing complex feature dependencies.
Efimovich et al. integrated deep learning with natural language processing for multilabel lung disease classification, thereby facilitating better alignment between radiological images and textual reports, enhancing clinical interpretability. Van der Sluijs et al. explored image augmentations in Siamese networks for chest X-ray representation learning, improving disease classification performance with contrastive learning techniques. Wang et al. proposed a multi-granularity cross-modal alignment approach for generalizable medical visual representation learning, enhancing the interpretability of chest X-ray analysis. Zhou et al. advanced radiograph representation learning using masked record modeling, which integrates textual clinical records with visual features for improved disease prediction. Ye et al. introduced continual self-supervised learning for universal multi-modal medical data representation, facilitating cross-domain transfer in chest radiograph analysis.
Gündel et al. proposed integrating external knowledge sources to improve chest abnormality classification under noisy labels, enhancing robustness and generalization. Nguyen et al. focused on multi-language radiology report mining, building a deep learning model for thoracic disease diagnosis from Vietnamese chest radiographs. Jang et al. significantly improved zero-shot pathology classification on chest X-rays by fine-tuning image-text encoders, enabling scalable detection of unseen diseases. Vu et al. developed MedAug, a contrastive learning framework that leverages patient metadata to improve chest X-ray representations, thereby enhancing model robustness and transferability. Lian et al. proposed a DRD U-Net integrated with WGAN and deep neural networks for lung image segmentation, demonstrating improved boundary delineation in chest CT scans.
Xiao and Zhao developed a multi-task conditional GAN for synthesizing 3D liver CT images from vascular structures, illustrating the versatility of generative models in medical imaging. Ukwuoma et al. designed a hybrid explainable ensemble transformer encoder for pneumonia detection from chest X-rays, combining performance with interpretability. Sharmay et al. explored transfer learning in histopathology through the HistoTransfer framework, providing insights applicable to chest X-ray domain adaptation. Feng et al. applied deep supervised domain adaptation to improve pneumonia diagnosis across chest X-ray datasets with distributional differences. Lastly, Lee and Liu demonstrated a real-time end-to-end deep learning framework for lane detection and path prediction in autonomous driving, illustrating the effectiveness of multi-task learning beyond healthcare.
Table 1 summarizes key multi-task chest X-ray studies, highlighting their contributions and identifying research gaps addressed by the proposed framework. The reviewed literature demonstrates significant progress in multi-task learning, transformer-based models, and explainable AI for chest X-ray analysis. However, existing approaches often lack a unified framework that efficiently integrates disease localization and classification while ensuring clinical interpretability. Addressing these gaps, the proposed CXR-MultiTaskNet leverages joint learning and explainable modules to deliver accurate and transparent chest radiograph analysis.