Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised Pathology Segmentation with Disentangled Representations   Automated pathology segmentation remains a valuable diagnostic tool in
clinical practice. However, collecting training data is challenging.
Semi-supervised approaches by combining labelled and unlabelled data can offer
a solution to data scarcity. An approach to semi-supervised learning relies on
reconstruction objectives (as self-supervision objectives) that learns in a
joint fashion suitable representations for the task. Here, we propose
Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation
model that attempts to learn jointly for the first time: disentanglement of
anatomy, modality, and pathology. The model is trained in a semi-supervised
fashion with new reconstruction losses directly aiming to improve pathology
segmentation with limited annotations. In addition, a joint optimization
strategy is proposed to fully take advantage of the available annotations. We
evaluate our methods with two private cardiac infarction segmentation datasets
with LGE-MRI scans. APD-Net can perform pathology segmentation with few
annotations, maintain performance with different amounts of supervision, and
outperform related deep learning methods.

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The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg