Search Results for author:
Found 10 papers, 1 papers with code
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface
The system requires the shortest time and has the best performance for processing color images.
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints
To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions.
Universal Lymph Node Detection in T2 MRI using Neural Networks
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
Lymph Node Detection in T2 MRI with Transformers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans
It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.
Partial Identification and Inference for Conditional Distributions of Treatment Effects
This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates.
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement.
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Segmentation
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
