Back
Top 2%
18.1%
Top 4%
18.1%
Top 3%
6.6%
Top 71%
6.6%
Top 4%
6.1%
Top 10%
4.3%
Top 1%
4.0%
Top 2%
4.0%
Top 2%
4.0%
Top 7%
2.0%
Top 1%
1.6%
Top 1%
1.6%
Top 5%
1.4%
Top 9%
1.4%
Top 8%
1.0%
Top 12%
1.0%
Top 5%
0.7%
Top 20%
0.7%
Top 5%
0.7%
Top 6%
0.7%
Top 7%
0.7%
Benchmarking Transfer Learning for Dense Breast Tissue Segmentation on Small Mammogram Datasets
2026-02-24
radiology and imaging
Title + abstract only
View on medRxiv
Show abstract
Dense breast tissue diminishes the sensitivity of mammographic screening and is a key cancer risk factor, which motivates accurate segmentation under scarce and expensive expert annotations in the medical imaging domain. Here, we benchmark the effect of backbone architecture, self-supervised pre-training (SSL), fine-tuning strategy, and loss design for dense-tissue segmentation on a small expert-labeled dataset (596 images) and an in-domain unlabeled corpus (20, 000 images), reflecting the lack ...
Predicted journal destinations
1
Nature Communications
483 training papers
2
Scientific Reports
701 training papers
3
npj Digital Medicine
85 training papers
4
PLOS ONE
1737 training papers
5
PLOS Digital Health
88 training papers
6
eLife
262 training papers
7
NeuroImage
36 training papers
8
Human Brain Mapping
53 training papers
9
Computers in Biology and Medicine
39 training papers
10
PLOS Computational Biology
141 training papers
11
Imaging Neuroscience
18 training papers
12
Scientific Data
30 training papers
13
Journal of Biomedical Informatics
37 training papers
14
NeuroImage: Clinical
77 training papers
15
eBioMedicine
82 training papers
16
Nature Medicine
88 training papers
17
Alzheimer's Research & Therapy
31 training papers
18
Proceedings of the National Academy of Sciences
100 training papers
19
Science Advances
52 training papers
20
Diagnostics
36 training papers
21
Communications Medicine
63 training papers