Authors:
Daisuke Matsuzuki
and
Kazuhiro Hotta
Affiliation:
Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Keyword(s):
Cell Image Segmentation, Difficult Pixels, Visualization, Modification, U-net.
Abstract:
In this paper, we visualize and modify difficult pixels to recognize for deep learning. In general, an image includes pixels that are easy or difficult to recognize. At the final layer, many deep learning methods use a softmax function to convert the outputs of network to probabilities. Pixels with small maximum probability are often difficult to recognize. We visualize those difficult pixels in a test image using the relationship between confidence and pixel-wise difficulty. By visualizing difficult pixels, we confirm the connection of cell membrane that could not be recognized by conventional method. We can connect the cell membrane by modifying difficult pixels. In experiments, we use cell image of mouse liver dataset including three classes; “cell membrane”, “cell nucleus” and “cytoplasm”. Our proposed method shows high recall score for “cell membrane”. We also confirmed the connection of cell membrane in qualitative evaluation.