DCSNet combines detection-guided hierarchical cropping with multiscale feature aggregation inside cropped regions to improve small medical object segmentation.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation, April 2020
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.
citing papers explorer
-
DCSNet: Multiscale Feature Aggregation for Small Medical Object Segmentation with Detection-guided Hierarchical Cropping
DCSNet combines detection-guided hierarchical cropping with multiscale feature aggregation inside cropped regions to improve small medical object segmentation.
-
Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.