BMAI
バイオメディカルAI研究ユニット
バイオメディカルAI研究ユニット
Ja
バイオメディカルAI研究ユニットは2021年6月に設立されました。 Read more...
研究歴：
1993年- (株) 日立メディコ 技術研究所
1997年- 愛知県立大学 情報科学部
2001年- シカゴ大学 放射線学科/大学院医用物理学研究科/がん研究センター
2014年- イリノイ工科大学 医用画像研究所/電気電子工学研究科
2017年- 東京工業大学 科学技術創成研 Read more...
サーバー室
研究用GPUサーバー 21台
- i9-10980XE 18-Core/3.0GHz; RTX A6000x2; 128GB MEM; SSD 3.8TBx4 HDD 18TBx2
- i9-10940X 14-Core/3.3GHz; RTX 3090×2; 64GB MEM; SSD 960GB
- i9-10940X 14-Core/3.3GHz; RTX A6000; 64GB MEM; SSD 480GB
- EPYC 7502P 32-Core/2.5GHz; RTX A6000x Read more...
Tracing of vessels is one of the most fundamental techniques in a computer-aided diagnostic (CAD) scheme for vascular systems. A major challenge of methods for tracing vessels is to trace vessels robustly against noise, vessel branching, vessel size changes, and curved vessels, because those factors often lead to errors in tracing. Among them, the robustness against vessel size changes is espec Read more...
We developed a method for extracting the left ventricular (LV) contours from left ventriculograms by means of a neural edge detector (NED) in order to extract the contours which agree with those traced by a cardiologist. The NED is a supervised edge detector based on a linear-output artificial neural network model, which is trained with a modified back-propagation training algorithm. The NED ca Read more...
We developed a supervised nonlinear filter based on an artificial neural network (ANN), called a “neural filter,” for reduction of quantum noise in coronary angiograms. The neural filter can be trained with input images and the corresponding teaching images. To learn the relationship between low-dose and high-dose x-ray images, we created simulated low-dose angiograms from actual high-dose Read more...
We developed a new computer-aided diagnostic (CAD) system for coronary artery stenosis, which can learn physicians’ diagnosis. To realize such a system, we developed a linear-output artificial neural network (LOANN) which is capable of learning of experts’ judgment. Our CAD system consisted of (a) an automated method for tracing vessels, (b) a robust method for determining the edges of the Read more...
We evaluated our virtual dual-energy radiography technique based on a massive-training artificial neural network (MTANN) in the improvement of the conspicuity of nodules in chest radiographs. To do this, we used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 Read more...
When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this study, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multi-resolution massive-training artificial neural network (MTANN).< Read more...