Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised Multi-Task Learning for Cell Detection and
  Segmentation   Cell detection and segmentation is fundamental for all downstream analysis of
digital pathology images. However, obtaining the pixel-level ground truth for
single cell segmentation is extremely labor intensive. To overcome this
challenge, we developed an end-to-end deep learning algorithm to perform both
single cell detection and segmentation using only point labels. This is
achieved through the combination of different task orientated point label
encoding methods and a multi-task scheduler for training. We apply and validate
our algorithm on PMS2 stained colon rectal cancer and tonsil tissue images.
Compared to the state-of-the-art, our algorithm shows significant improvement
in cell detection and segmentation without increasing the annotation efforts.

6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th