U-Net

https://gyazo.com/123daba5c408b269337944b970968058

There is large consent that successful training of deep networks requires many thousand annotated training samples.

The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization.

We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin.

Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at this http URL .

2015

... 画像を同じサイズの別の画像に変換するために良く使われるニューラルネットワークです。

Uの字のような構造

エンコーダーで徐々に高抽象度・低解像度の情報を抽出し、

デコーダーで元の解像度に戻す