Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised
  Learning Through Hierarchical Contrastive Learning   In computational pathology, we often face a scarcity of annotations and a
large amount of unlabeled data. One method for dealing with this is
semi-supervised learning which is commonly split into a self-supervised pretext
task and a subsequent model fine-tuning. Here, we compress this two-stage
training into one by introducing S5CL, a unified framework for
fully-supervised, self-supervised, and semi-supervised learning. With three
contrastive losses defined for labeled, unlabeled, and pseudo-labeled images,
S5CL can learn feature representations that reflect the hierarchy of distance
relationships: similar images and augmentations are embedded the closest,
followed by different looking images of the same class, while images from
separate classes have the largest distance. Moreover, S5CL allows us to
flexibly combine these losses to adapt to different scenarios. Evaluations of
our framework on two public histopathological datasets show strong improvements
in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the
accuracy increases by up to 9% compared to supervised cross-entropy loss; for a
highly imbalanced dataset of single white blood cells from leukemia patient
blood smears, the F1-score increases by up to 6%.

17
The categories of this paper are: q-fin
2
The categories of this paper are: math
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph