Fujitsu Laboratories has announced the development of artificial intelligence (AI) technology that uses deep learning to detect objects, even in cases when only a small amount of data is available for learning.
In recent years, there have been efforts to automate tasks in a variety of fields. In the medical field, for example, there has been a desire to use AI to automate such tasks as detecting objects, including anomalous spots, in the analysis of diagnostic images.
It is typical to utilise deep learning in object detection, which involves identifying specific structures in a diagnostic image, but in order to produce accurate results, tens of thousands of images with correct data are necessary.
However, since correct data can only be created by doctors with expert knowledge, it has been difficult to obtain the images in such huge volumes.
Now Fujitsu Laboratories has developed a technology (patent pending) that takes the object location estimates produced by the object detection neural network and makes them into a reconstruction of the original image. By assessing the difference between the original input image and the reconstructed image, it can create large volumes of correct data where the position of objects has been accurately estimated. This raises the level of accuracy in object detection.
Fujitsu Laboratories has collaborated with the Graduate School of Medicine at Kyoto University and applied the newly developed technology to the detection of bodies called glomeruli (singular glomerulus) in kidney biopsy images.
The results of an evaluation showed that in an experiment using 50 images with correct data and 450 images without correct data, compared with existing training methods using only the same number of images with correct data, the accuracy of the new technology had more than doubled, under the stipulation of an oversight rate of less than 10%.