
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 175715 - 175741
Published: Jan. 1, 2024
Language: Английский
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 175715 - 175741
Published: Jan. 1, 2024
Language: Английский
Physics in Medicine and Biology, Journal Year: 2024, Volume and Issue: 69(11), P. 11TR01 - 11TR01
Published: March 13, 2024
Abstract Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role computer-aided diagnosis, surgical simulation, image-guided interventions, and especially radiotherapy treatment planning. Thus, it is great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly witnessed remarkable progress multi-organ segmentation. However, obtaining appropriately sized fine-grained annotated dataset extremely hard expensive. Such scarce annotation limits development high-performance models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer leveraging external datasets, semi-supervised including unannotated datasets partially-supervised integrating partially-labeled led dominant way break such dilemmas We first review fully supervised method, then present a comprehensive systematic elaboration 3 abovementioned paradigms context both technical methodological perspectives, finally summarize their challenges future trends.
Language: Английский
Citations
5Measurement, Journal Year: 2025, Volume and Issue: 248, P. 116920 - 116920
Published: Feb. 7, 2025
Language: Английский
Citations
0Pattern Recognition, Journal Year: 2025, Volume and Issue: 167, P. 111707 - 111707
Published: April 26, 2025
Language: Английский
Citations
0Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 163809 - 163809
Published: May 1, 2025
Language: Английский
Citations
0IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal Year: 2024, Volume and Issue: 46(12), P. 7909 - 7923
Published: May 6, 2024
Continual
semantic
segmentation
(CSS)
based
on
incremental
learning
(IL)
is
a
great
endeavour
in
developing
human-
like
models.
However,
current
CSS
approaches
encounter
challenges
the
trade-off
between
preserving
old
knowledge
and
new
ones,
where
they
still
need
large-scale
annotated
data
for
training
lack
interpretability.
In
this
paper,
we
present
Language: Английский
Citations
2IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 175715 - 175741
Published: Jan. 1, 2024
Language: Английский
Citations
1