Optical Methods for Brain Tumor Detection: A Systematic Review.

In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues.

A historical perspective of biomedical explainable AI research.

The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research.

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training.

Diffuse reflectance spectroscopy sensor to differentiate between glial tumor and healthy brain tissue: a proof-of-concept study.

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis.