IEEE

DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation

Abstract: Semi-supervised learning in medical image segmentation leverages unlabeled data to reduce annotation burdens through consistency learning. However, current methods struggle with class ...
The Lancet

Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA…

We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...
Security Boulevard

HPE’s Post-Juniper Vision: AI-Driven Security at Enterprise Scale

Integration Complete At Security Field Day, Hewlett Packard Enterprise showcased the results of its most ambitious integration ...

Advancing AI By Nesting Minds Inside The Layers Of Machine Learning And LLMs

Prevailing AI architectures are not moving the needle. We need new ideas. Google Research proposes NL (nested learning). Here ...
IEEE

Comparative Analysis of Deep Learning-Based Models for Blood Cell Image Segmentation

Abstract: The accurate segmentation of blood cells is crucial in determining a wide range of hematological disorders. Therefore, in this paper, we investigated the most accurate and efficient blood ...