Explore >> Select a destination
|
You are here 
|
www.ntentional.com | ||
| | | | |
iclr.cc
|
|
| | | | | ||
| | | | |
iclr.cc
|
|
| | | | | ||
| | | | |
www.paepper.com
|
|
| | | | | Illustration taken from the U-Net paper I recently read an interesting paper titled "U-Net: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox which describes how to handle challenges in image segmentation in biomedical settings which I summarize in this blog post. Challenges for medical image segmentation A typical task when confronted with medical images is segmentation. That refers to finding out interesting objects in an image. | |
| | | | |
alexanderganderson.github.io
|
|
| | | |||
