Evidence map›Paper›PMID 42325283›Full record

ArticleFrontiers in artificial intelligence2026

CA

Aman Kumar Singh, Ashish Ranjan, Manas Ranjan Prusty, Nitish Katal

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Aman Kumar SinghSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Ashish RanjanSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Manas Ranjan PrustyCentre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
Nitish KatalCentre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objectives: Accurate medical image segmentation remains a challenging task in computer-aided diagnosis because of the intricacies and the variability in the biomedical data in terms of the anatomical complexity, inter-patient diversity, class imbalance, and irregular morphological patterns. Methods: In the present work, a Context Aware Adaptive Progressive Network (CA Results: CA Conclusions: The proposed work demonstrates that the embedding of multi-scale features at each encoder stage along with decoupled decoding aids in overcoming the limitation of resolution loss in classical segmentation architectures; thus, resulting in superior boundary adherence and scale-invariant segmentation.

Indexed as

attention mechanismsbreast tumor segmentationcontext-aware feature extractiondilated convolutionsmedical image segmentationmulti-scale representation learningpolyp segmentation

Identifiers

PMID42325283
PMCPMC13279314

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.