We investigate the use of recent advances in deep learning and propose an end-to-end trainable multi-instance convolutional neural network within a mixture-of-experts formulation that combines information from two types data-images clinical attributes-for diagnosis lymphocytosis. The learns to extract meaningful features images blood cells using embedding level approach aggregates them. Moreove...

in this paper, a multi-period and multi-resource operating room (or) scheduling and rescheduling problem with elective and semi-elective (semi-urgent) patients is addressed. a scheduling-rescheduling framework based on the so-called rolling horizon approach is proposed to solve the problem. the core of the proposed framework is a novel proposed mixed-integer linear programming (milp) model with...