Purpose We propose a novel domain‐specific loss, which is a differentiable loss function
based on the dose‐volume histogram (DVH), and combine it with an adversarial loss for the …

Purpose Although dose prediction for intensity modulated radiation therapy (IMRT) has been
accomplished by a deep learning approach, delineation of some structures is needed for the …

Recently, artificial intelligence technologies and algorithms have become a major focus for
advancements in treatment planning for radiation therapy. As these are starting to become …

We develop a generalized inverse optimization framework for fitting the cost vector of a
single linear optimization problem given multiple observed decisions. This setting is …

Radiotherapy treatment planning currently requires many trial-and-error iterations between
the planner and treatment planning system, as well as between the planner and physician …

Cancer remains one of the most challenging diseases to treat in the medical field. Machine
learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for …

Radiation treatment being a crucial step in cancer treatment, accurate radiation treatment
planning is extremely important to reduce the effect of radiation on Organs-At-Risk (OAR). In …

The field of radiation therapy is ever-changing, with technologies and distributions of patient
populations evolving over time. Some of the largest advancements in the past few decades …

We develop a generalized inverse optimization framework for fitting the cost vector of a
single linear optimization problem given an ensemble of observed decisions. We unify …

In recent years, deep learning has seen its potential significantly increased in the medical
field, with various applications making the work of physicians more automated and efficient …