Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
Making the most of text semantics to improve biomedical vision–language processing
Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …
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 …
Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …
WRENCH: A comprehensive benchmark for weak supervision
Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
Robust and efficient medical imaging with self-supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset
The reliability of machine learning models can be compromised when trained on low quality
data. Many large-scale medical imaging datasets contain low quality labels extracted from …
data. Many large-scale medical imaging datasets contain low quality labels extracted from …
Ontology-driven weak supervision for clinical entity classification in electronic health records
JA Fries, E Steinberg, S Khattar, SL Fleming… - Nature …, 2021 - nature.com
In the electronic health record, using clinical notes to identify entities such as disorders and
their temporality (eg the order of an event relative to a time index) can inform many important …
their temporality (eg the order of an event relative to a time index) can inform many important …