Publications by authors named "Taro Watasue"
Article Synopsis
- Computer-aided diagnostic methods, particularly using deep learning, have significantly advanced the automatic detection of liver tumors through multi-phase CT images, improving healthcare outcomes.
- A key challenge in these methods is the need for large amounts of high-quality annotated training data, which is often difficult to obtain in medical imaging.
- To overcome this, a new adversarial learning strategy is proposed that utilizes Fourier phase components of CT images, enhancing semantic information and eliminating the need for separate annotations for different scan phases, resulting in improved detection performance.
Automatic and efficient liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis of liver tumors. Nowadays, deep learning has been widely used in medical applications. Normally, deep learning-based AI systems need a large quantity of training data, but in the medical field, acquiring sufficient training data with high-quality annotations is a significant challenge.
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