At a glance
Research topics
The technical topics below describe the original research direction, not validated clinical results.
- Research topic
- Oncology imaging and tumor segmentation
- Methods discussed
- Quantum neural networks (QNNs) and quantum convolutional neural networks (QCNNs)
- Dataset named
- BraTS2020 Glioma MRI
- Original publication
- January 8, 2025 · Michelle Wu
Why Oncology Research Matters to NEXQ
TumorQ grew out of Michelle Wu's interest in oncology and the experiences of people close to her who faced cancer. Her original article identifies diagnosis and tumor characterization as the problems she wanted to investigate.
She describes the emotional and physical toll of diagnostic delays on patients and their families as part of her motivation for starting the project.
The Human Reason Behind TumorQ
Michelle wrote about losing loved ones to cancer and watching relatives and friends receive late-stage diagnoses. Those experiences shaped her interest in oncology.
In her January 2025 article, she described undergraduate studies at UC Riverside, mentorship from experts at UC Berkeley and MIT, and work with City of Hope on glioblastoma drug delivery.
Her account connects those experiences with an interest in chemistry, biomedical engineering, applied mathematics, and quantum mechanics.
The Research Direction
The original TumorQ article discusses quantum neural networks (QNNs), quantum convolutional neural networks (QCNNs), and tumor-region segmentation. It names the BraTS2020 Glioma MRI dataset as a research reference.
These topics describe the project's research direction. This summary does not report a validated clinical study or establish the performance of a diagnostic product.
Mission and Vision
Michelle's stated aim is to bring quantum computing and medical research together while keeping patients and their families at the center of the work.
The original article identifies precision, compassion, and data privacy as priorities for the project.