Nov 9, 2021 · Abstract:In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions.
Nov 10, 2021 · The purpose of this work is to design an ensemble-based universal lesion detector composed of state-of-the-art detection networks to ...
Universal lesion detection in CT scans using neural network ensembles
www.semanticscholar.org › paper
This work proposes the use of state-of-the-art detection neural networks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing and ...
This work proposes to improve the outcome of automatic diagnoses approaches by using an ensemble of pre-trained deep convolutional neural networks and a ...
Nov 9, 2021 · To overcome these challenges, we propose the use of state-of-the-art detection neuralnetworks to flag suspicious lesions present in the NIH ...
In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the ...
Universal Lesion Detection in CT Scans using Neural Network Ensembles ... In clinical practice, radiologists are reliant on the lesion size when distinguishing ...
Jul 4, 2024 · Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, ...
This work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts and ...
Additionally, neural networks have been employed for lesion detection in CT scans, aiding radiologists in identifying suspicious lesions for further assessment ...