30 papers with code • 0 benchmarks • 0 datasets
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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.
We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.
To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.
However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis.
Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis.
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field.
In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge.