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Developing a Novel Image Marker to Predict the Clinical Outcome of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients
To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage.
Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapy
2D and 3D tumor features are widely used in a variety of medical image analysis tasks.
Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms
For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts.
Virtual Adversarial Training for Semi-supervised Breast Mass Classification
This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning.
Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis.
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme.
