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Found 4 papers, 1 papers with code
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging
We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and feature encoding configurations.
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
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