Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Learning for Mitochondria Segmentation   Mitochondria segmentation in electron microscopy images is essential in
neuroscience. However, due to the image degradation during the imaging process,
the large variety of mitochondrial structures, as well as the presence of
noise, artifacts and other sub-cellular structures, mitochondria segmentation
is very challenging. In this paper, we propose a novel and effective
contrastive learning framework to learn a better feature representation from
hard examples to improve segmentation. Specifically, we adopt a point sampling
strategy to pick out representative pixels from hard examples in the training
phase. Based on these sampled pixels, we introduce a pixel-wise label-based
contrastive loss which consists of a similarity loss term and a consistency
loss term. The similarity term can increase the similarity of pixels from the
same class and the separability of pixels from different classes in feature
space, while the consistency term is able to enhance the sensitivity of the 3D
model to changes in image content from frame to frame. We demonstrate the
effectiveness of our method on MitoEM dataset as well as FIB-SEM dataset and
show better or on par with state-of-the-art results.

17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat