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Opportunities and obstacles for deep learning in biology and medicine
Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …
combining raw inputs into layers of intermediate features. These algorithms have recently …
Vision-language models for medical report generation and visual question answering: A review
Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …
Medclip: Contrastive learning from unpaired medical images and text
Existing vision-text contrastive learning like CLIP (Radford et al., 2021) aims to match the
paired image and caption embeddings while pushing others apart, which improves …
paired image and caption embeddings while pushing others apart, which improves …
A medical multimodal large language model for future pandemics
Deep neural networks have been integrated into the whole clinical decision procedure
which can improve the efficiency of diagnosis and alleviate the heavy workload of …
which can improve the efficiency of diagnosis and alleviate the heavy workload of …
Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis
In this paper, we consider enhancing medical visual-language pre-training (VLP) with
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Large, labeled datasets have driven deep learning methods to achieve expert-level
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Chest radiography is an extremely powerful imaging modality, allowing for a detailed
inspection of a patient's thorax, but requiring specialized training for proper interpretation …
inspection of a patient's thorax, but requiring specialized training for proper interpretation …
Radgraph: Extracting clinical entities and relations from radiology reports
Extracting structured clinical information from free-text radiology reports can enable the use
of radiology report information for a variety of critical healthcare applications. In our work, we …
of radiology report information for a variety of critical healthcare applications. In our work, we …
Feasibility of using the privacy-preserving large language model Vicuna for labeling radiology reports
Background Large language models (LLMs) such as ChatGPT, though proficient in many
text-based tasks, are not suitable for use with radiology reports due to patient privacy …
text-based tasks, are not suitable for use with radiology reports due to patient privacy …
Padchest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the automated
exploration of medical images along with their associated reports. This dataset includes …
exploration of medical images along with their associated reports. This dataset includes …