02/02/2021

A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis

Ying Zhang, Yifang Yin, Zhenguang Liu, Roger Zimmermann

Keywords:

Abstract Paper Similar Papers

Abstract: Cervical dysplasia diagnosis via visual investigation is a challenging problem. Recent approaches use deep learning techniques to extract features and require the downsampling of high-resolution cervical screening images to smaller sizes for training. Such a reduction may result in the loss of visual details that appear weakly and locally within a cervical image. To overcome this challenge, our work divides an image into patches and then represents it from patch features. We aggregate patch patterns into an image feature in a weighted manner by considering the patch--image label relation. The weights are visualized as a heatmap to explain where the diagnosis results come from. We further introduce a spatial regulator to guide the classifier to focus on the cervix region and to adjust the weight distribution, without requiring any manual annotations of the cervix region. A novel iterative algorithm is designed to refine the regulator, which is able to capture the variations in cervix center locations and shapes. Experiments on an 18-year real-world dataset indicate a minimal of 3.47%, 4.59%, 8.54% improvements over the state-of-the-art in accuracy, F1, and recall measures, respectively.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38947938
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 9:15 
 0
 0
 0
 0
 14:05 
 0
 0
 0
 0
 11:25 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 5:09 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 9:58 
 0
 0
 0
 0
 17:00 
 0
 0
 0
 0
 4:48 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 4:42 
 0
 0
 0
 0
 2:55 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 4:32 
 0
 0
 0
 0
 5:09 
 0
 0
 0
 0
 14:40 
 0
 0
 0
 0
 4:52 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 3:36 
 0
 0
 0
 0
 13:34 
 0
 0
 0
 0
 16:29 
 0
 0
 0
 0
 6:53 
 0
 0
 0
 0
 4:24 
 1
 1
 0
 0
 1:01 
 0
 0
 0
 0
 5:52 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 1
 5:00 
 0
 0
 0
 0
 13:07 
 0
 0
 0
 0
 16:37 
 0
 1
 0
 0
 4:59 
 0
 0
 0
 0
 2:40 
 0
 0
 0
 0
 14:31 
 0
 0
 0
 0
 11:58 
 0
 0
 0
 0
 9:22 
 0
 0
 0
 0
 5:03