[Retracted] Lung Disease Classification in CXR Images Using Hybrid Inception‐ResNet‐v2 Model and Edge Computing
CM Sharma, L Goyal, VM Chariar… - Journal of Healthcare …, 2022 - Wiley Online Library
Chest X‐ray (CXR) imaging is one of the most widely used and economical tests to
diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge …
diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge …
Cross-modal contrastive attention model for medical report generation
Medical report automatic generation has gained increasing interest recently as a way to help
radiologists write reports more efficiently. However, this image-to-text task is rather …
radiologists write reports more efficiently. However, this image-to-text task is rather …
Clinically labeled contrastive learning for oct biomarker classification
K Kokilepersaud, ST Corona… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
This article presents a novel positive and negative set selection strategy for contrastive
learning of medical images based on labels that can be extracted from clinical data. In the …
learning of medical images based on labels that can be extracted from clinical data. In the …
A survey on advancements in image-text multimodal models: From general techniques to biomedical implementations
With the significant advancements of Large Language Models (LLMs) in the field of Natural
Language Processing (NLP), the development of image-text multimodal models has …
Language Processing (NLP), the development of image-text multimodal models has …
Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching
Traffic accidents threaten human lives and impose substantial financial burdens annually.
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …
A survey on image-text multimodal models
With the significant advancements of Large Language Models (LLMs) in the field of Natural
Language Processing (NLP), the development of image-text multimodal models has …
Language Processing (NLP), the development of image-text multimodal models has …
Enhancing Neural Text Detector Robustness with μAttacking and RR-Training
With advanced neural network techniques, language models can generate content that
looks genuinely created by humans. Such advanced progress benefits society in numerous …
looks genuinely created by humans. Such advanced progress benefits society in numerous …
Multimodal Foundation Models for Medical Imaging-A Systematic Review and Implementation Guidelines
Advancements in artificial intelligence (AI) offer promising solutions for enhancing clinical
workflows and patient care, potentially revolutionizing healthcare delivery. However, the …
workflows and patient care, potentially revolutionizing healthcare delivery. However, the …
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT
J Zheng, R Wen, H Hu, L Wei, K Su, W Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Existing Masked Image Modeling (MIM) depends on a spatial patch-based masking-
reconstruction strategy to perceive objects' features from unlabeled images, which may face …
reconstruction strategy to perceive objects' features from unlabeled images, which may face …
Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set
Background Enteral nutrition through feeding tubes serves as the primary method of
nutritional supplementation for patients unable to feed themselves. Plain radiographs are …
nutritional supplementation for patients unable to feed themselves. Plain radiographs are …