Diagnostic Radiology

Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

Medical image segmentation remains a formidable challenge due to the label scarcity. Pre-training Vision Transformer (ViT) through masked image modeling (MIM) on large-scale unlabeled medical datasets presents a promising solution, providing both computational efficiency and model generalization for various downstream tasks. However, current ViT-based MIM pre-training frameworks predominantly em...

Multi-Scale Transformer Architecture for Accurate Medical Image Classification

This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medical image analysis. By integrating a multi-scale feature fusion mechanism and refining the self-attention process, the model effectively extracts both global and local features, enhancing its ability to detect lesions w...

A Generative Framework for Bidirectional Image-Report Understanding in Chest Radiography

The rapid advancements in large language models (LLMs) have unlocked their potential for multimodal tasks, where text and visual data are processed ...

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation

Segmentation of 3D medical images is a critical task for accurate diagnosis and treatment planning. Convolutional neural networks (CNNs) have domina...

Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges

Deep learning techniques are increasingly being adopted in diagnostic medical imaging. However, the limited availability of high-quality, large-scal...

Foundation Models in Radiology: What, How, Why, and Why Not.

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating bot...

AI Applications for Thoracic Imaging: Considerations for Best Practice.

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thora...

An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training

The clinical adoption of artificial intelligence (AI) in medical imaging requires models that are both diagnostically accurate and interpretable to ...

AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatme...

A generalizable 3D framework and model for self-supervised learning in medical imaging

Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiti...

In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review

Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack...

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models li...

Vision Foundation Models for Computed Tomography

Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we de...

MIAFEx: An Attention-based Feature Extraction Method for Medical Image Classification

Feature extraction techniques are crucial in medical image classification; however, classical feature extractors in addition to traditional machine ...

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis

As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis...

MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives

We propose MedicalNarratives, a dataset curated from medical pedagogical videos similar in nature to data collected in Think-Aloud studies and inspi...

KM-UNet KAN Mamba UNet for medical image segmentation

Medical image segmentation is a critical task in medical imaging analysis. Traditional CNN-based methods struggle with modeling long-range dependenc...

Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) l...

Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research

Inflammatory bowel disease (IBD) research is a dynamic field. However, the growing volume of electronic health records (EHRs) and research data presen...

Physician-level classification performance across multiple imaging domains with a diagnostic medical foundation model and a large dataset of annotated medical images

A diagnostic medical foundation model (MedFM) is an artificial intelligence (AI) system engineered to accurately determine diagnoses across various me...