AI Medical Compendium Topic

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Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs.

IEEE transactions on medical imaging
Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a sma...

A scoping review on multimodal deep learning in biomedical images and texts.

Journal of biomedical informatics
OBJECTIVE: Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images an...

Annotated dataset creation through large language models for non-english medical NLP.

Journal of biomedical informatics
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often req...

Breast Cancer Histopathological Images Segmentation Using Deep Learning.

Sensors (Basel, Switzerland)
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which rem...

CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation.

Physics in medicine and biology
Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. O...

Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure.

Scientific reports
Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language mo...

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

Computers in biology and medicine
The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversa...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

BMJ open
OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manua...

Machine learning and image analysis in vascular surgery.

Seminars in vascular surgery
Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, de...

An End-to-End Framework for Joint Denoising and Classification of Hyperspectral Images.

IEEE transactions on neural networks and learning systems
Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification t...