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Found 5 papers, 2 papers with code
Accurately identifying vertebral levels in large datasets
We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains.
Generative Adversarial Network
Image-to-Image Translation
+2
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.
Ranked #7 on
Medical Object Detection
on DeepLesion
A self-attention based deep learning method for lesion attribute detection from CT reports
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
In radiologists' routine work, one major task is to read a medical image, e. g., a CT scan, find significant lesions, and describe them in the radiology report.
