Published on Wed Jan 29 2020

3D Aggregated Faster R-CNN for General Lesion Detection

Ning Zhang, Yu Cao, Benyuan Liu, Yan Luo

Lesions are damages and abnormalities in tissues of the human body. Many of them can later turn into fatal diseases such as cancers. Detecting lesions is of great importance for early diagnosis and timely treatment. CT scans often serve as the screening tool.

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Abstract

Lesions are damages and abnormalities in tissues of the human body. Many of them can later turn into fatal diseases such as cancers. Detecting lesions are of great importance for early diagnosis and timely treatment. To this end, Computed Tomography (CT) scans often serve as the screening tool, allowing us to leverage the modern object detection techniques to detect the lesions. However, lesions in CT scans are often small and sparse. The local area of lesions can be very confusing, leading the region based classifier branch of Faster R-CNN easily fail. Therefore, most of the existing state-of-the-art solutions train two types of heterogeneous networks (multi-phase) separately for the candidate generation and the False Positive Reduction (FPR) purposes. In this paper, we enforce an end-to-end 3D Aggregated Faster R-CNN solution by stacking an "aggregated classifier branch" on the backbone of RPN. This classifier branch is equipped with Feature Aggregation and Local Magnification Layers to enhance the classifier branch. We demonstrate our model can achieve the state of the art performance on both LUNA16 and DeepLesion dataset. Especially, we achieve the best single-model FROC performance on LUNA16 with the inference time being 4.2s per processed scan.

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Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple neighboring slices, 3D context modeling is of great significance for developing algorithms. We propose a Modified.Pseudo-3D Feature Pyramid Network (MP3
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Tue Jul 09 2019
Computer Vision
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Lesion detection from computed tomography (CT) scans is challenging because of small lesion size and small inter-class variation. To tackle both problems, we need to enrich the feature representation and improve the feature discriminativeness.
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Fri Jan 18 2019
Computer Vision
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. intensity and texture) between lesions and otherissues. This work builds a Universal Lesion Detector (ULDor) based
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Mon Aug 12 2019
Computer Vision
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions.
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Wed Jun 05 2019
Machine Learning
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. We propose a highly accurate and efficient one-stage lesion detector.
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Sun Aug 30 2020
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Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
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