Deep learning (DL) models have reached remarkable achievements in medical imaging, but their performance heavily depends on the availability of large and diverse datasets. To overcome this limitation, transfer learning has emerged as a widely adopted solution, where models pretrained on large datasets are fine-tuned for specific medical tasks. Due to the scarcity of large-scale medical imaging datasets, most existing models are pretrained on natural image datasets such as ImageNet, while recent studies have explored highly complex models trained on large collections of medical unlabelled datasets. In this work, we generate and leverage a synthetic MRI dataset to pretrain DL architectures, demonstrating effective model learning with minimal computational cost. We evaluate our approach across multiple downstream tasks, including brain tumour classification and benchmark datasets from MedMNIST, including both 2D and 3D imaging modalities. Compared with ImageNet-pretrained, foundation, and self-supervised models, synthetic pretraining consistently improves feature representations and downstream performance. Overall, our approach outperforms competing methods, establishing a new state-of-the-art on the MedMNIST benchmark.
Rosanna Turrisi, Giuseppe Patané· Scientific Reports· 0 citations
BACKGROUND
The management of drug administration to patients promotes the customisation and accuracy of the treatments, reducing the risk of ineffective therapies and negative drug effects, and improving drug efficiency and management.
METHODS
We propose a graph representation learning model for predicting drug administration using the MIMIC-III database, which contains over 53K critical care admissions. We design a heterogeneous, weighted, directed, and multi-feature graph from patient demographics, diagnoses, and drug administration records. Our method uses a graph convolutional network to process node and edge features, predicting edge connections between patients and drugs. This choice enables us to analyse database properties, such as the relationship between drug administration and demographic classes, as well as the prediction accuracy with respect to drug occurrence.
RESULTS
We analyse the results in terms of correct, false positive, and false negative edge predictions of drug-patient administration. Our method has an accuracy of [Formula: see text] on the MIMIC-III database, with an F1-score of [Formula: see text]. We discuss the training results regarding convergence and execution time, analyse the accuracy on low-occurrence drugs, and compare our method with previous work.
CONCLUSION
Previous work has focused primarily on query and classification tasks for feature extraction and diagnosis prediction, achieving comparable accuracy but is often limited to specific pathologies (e.g., diabetes), patient groups (e.g., pregnant women), or drug types (e.g., antibiotics). Our approach processes heterogeneous data encompassing various patient characteristics, pathologies, and drug types, providing a more comprehensive and scalable solution.
Simone Cammarasana, Giuseppe Patané· BMC Medical Informatics and...· 0 citations
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