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Found 7 papers, 2 papers with code
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports
We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification.
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models
Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers.
Ranked #1 on
Medical Visual Question Answering
on OVQA
X-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation
In this paper we mimic this ability by using multi-modal retrieval augmentation and apply it to several tasks in chest X-ray analysis.
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
LifeLonger: A Benchmark for Continual Disease Classification
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis.
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning
Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures.
