AI Medical Compendium Topic:
Diagnostic Imaging

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Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification perform...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Die Ophthalmologie
Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial use of artificial intelligence (AI). The analysis of images and data by trained algorithms has the potential to facilitate making the diagnosis and pat...

A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models.

International journal of medical informatics
BACKGROUND: Medical imaging is a vital diagnostic tool that provides detailed insights into human anatomy but faces challenges affecting its accuracy and efficiency. Advanced generative AI models offer promising solutions. Unlike previous reviews wit...

Dual-Filter Cross Attention and Onion Pooling Network for Enhanced Few-Shot Medical Image Segmentation.

Sensors (Basel, Switzerland)
Few-shot learning has demonstrated remarkable performance in medical image segmentation. However, existing few-shot medical image segmentation (FSMIS) models often struggle to fully utilize query image information, leading to prototype bias and limit...

Demographic bias of expert-level vision-language foundation models in medical imaging.

Science advances
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit trainin...

Multimodal generative AI for medical image interpretation.

Nature
Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as mul...

Revisiting medical image retrieval via knowledge consolidation.

Medical image analysis
As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical ...

CLIP in medical imaging: A survey.

Medical image analysis
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretabil...

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence.

Seminars in nuclear medicine
Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the quality of life. The detection of low-grade inflammation and occult infections remains challenging. N...

A novel framework for segmentation of small targets in medical images.

Scientific reports
Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical i...