DeepAI AI Chat
Log In Sign Up

3D Anchor-Free Lesion Detector on Computed Tomography Scans

by   Ning Zhang, et al.
UMass Lowell

Lesions are injuries and abnormal tissues in the human body. Detecting lesions in 3D Computed Tomography (CT) scans can be time-consuming even for very experienced physicians and radiologists. In recent years, CNN based lesion detectors have demonstrated huge potentials. Most of current state-of-the-art lesion detectors employ anchors to enumerate all possible bounding boxes with respect to the dataset in process. This anchor mechanism greatly improves the detection performance while also constraining the generalization ability of detectors. In this paper, we propose an anchor-free lesion detector. The anchor mechanism is removed and lesions are formalized as single keypoints. By doing so, we witness a considerable performance gain in terms of both accuracy and inference speed compared with the anchor-based baseline


An Efficient Anchor-free Universal Lesion Detection in CT-scans

Existing universal lesion detection (ULD) methods utilize compute-intens...

Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks

Lesions characterized by computed tomography (CT) scans, are arguably of...

DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection

Incorporating data-specific domain knowledge in deep networks explicitly...

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

Accurate, automated lesion detection in Computed Tomography (CT) is an i...

3D Aggregated Faster R-CNN for General Lesion Detection

Lesions are damages and abnormalities in tissues of the human body. Many...

Bounding Maps for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essenti...

Lesion Detection on Leaves using Class Activation Maps

Lesion detection on plant leaves is a critical task in plant pathology a...