Deep Learning-Based Tech Capable of Training with Limited Data

Fujitsu Laboratories Ltd. has developed an artificial intelligence (AI) based technology that uses deep learning to detect objects, even when only a small amount of data is available for learning. According to the company, it is typical to utilize 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.

 

Fujitsu Laboratories patent-pending technology takes the object location estimates produced by the object detection neural network and creates a reconstruction of the original image. Then, 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. Reportedly, this raises the level of accuracy in object detection.

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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%.

 

For more details, visit Fujitsu.