Efficient Representation Learning for Healthcare with Cross-Architectural Self-Supervision

In healthcare and biomedical applications, extreme computational requirements pose a significant barrier to adopting representation learning. Representation learning can enhance the performance of deep learning architectures by learning useful priors from limited medical data. However, state-of-the-art self-supervised techniques suffer from reduced performance when using smaller batch sizes or shorter pretraining epochs, which are more practical in clinical settings. We present Cross Architectural - Self Supervision (CASS) in response to this challenge. This novel siamese self-supervised learning approach synergistically leverages Transformer and Convolutional Neural Networks (CNN) for efficient learning. Our empirical evaluation demonstrates that CASS-trained CNNs and Transformers outperform existing self-supervised learning methods across four diverse healthcare datasets. With only 1% labeled data for finetuning, CASS achieves a 3.8% average improvement; with 10% labeled data, it gains 5.9%; and with 100% labeled data, it reaches a remarkable 10.13% enhancement. Notably, CASS reduces pretraining time by 69% compared to state-of-the-art methods, making it more amenable to clinical implementation. We also demonstrate that CASS is considerably more robust to variations in batch size and pretraining epochs, making it a suitable candidate for machine learning in healthcare applications.Comment: Accepted at MLHC 2023. Extended conference version of arXiv:2206.0417

Transport and dosimetric solutions for the ELIMED laser-driven beam line

Within 2017, the ELIMED (ELI-Beamlines MEDical applications) transport beam-line and dosimetric systems for laser-generated beams will be installed at the ELI-Beamlines facility in Prague (CZ), inside the ELIMAIA (ELI Multidisciplinary Applications of laser-Ion Acceleration) interaction room. The beam-line will be composed of two sections: one in vacuum, devoted to the collecting, focusing and energy selection of the primary beam and the second in air, where the ELIMED beam-line dosimetric devices will be located. This paper briefly describes the transport solutions that will be adopted together with the main dosimetric approaches. In particular, the description of an innovative Faraday Cup detector with its preliminary experimental tests will be reported

Spontaneous Ignition of Cryo-Compressed Hydrogen in a T-Shaped Channel System

Sudden releases of pressurised hydrogen may spontaneously ignite by the so-called “diffusion ignition” mechanism. Several experimental and numerical studies have been performed on spontaneous ignition for compressed hydrogen at ambient temperature. However, there is no knowledge of the phenomenon for compressed hydrogen at cryogenic temperatures. The study aims to close this knowledge gap by performing numerical experiments using a computational fluid dynamics model, validated previously against experiments at atmospheric temperatures, to assess the effect of temperature decrease from ambient 300 K to cryogenic 80 K. The ignition dynamics is analysed for a T-shaped channel system. The cryo-compressed hydrogen is initially separated from the air in the T-shaped channel system by a burst disk (diaphragm). The inertia of the burst disk is accounted for in the simulations. The numerical experiments were carried out to determine the hydrogen storage pressure limit leading to spontaneous ignition in the configuration under investigation. It is found that the pressure limit for spontaneous ignition of the cryo-compressed hydrogen at temperature 80 K is 9.4 MPa. This is more than 3 times larger than pressure limit for spontaneous ignition of 2.9 MPa in the same setup at ambient temperature of 300 K