4,907 research outputs found
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
In this work, we tackle the problem of instance segmentation, the task of
simultaneously solving object detection and semantic segmentation. Towards this
goal, we present a model, called MaskLab, which produces three outputs: box
detection, semantic segmentation, and direction prediction. Building on top of
the Faster-RCNN object detector, the predicted boxes provide accurate
localization of object instances. Within each region of interest, MaskLab
performs foreground/background segmentation by combining semantic and direction
prediction. Semantic segmentation assists the model in distinguishing between
objects of different semantic classes including background, while the direction
prediction, estimating each pixel's direction towards its corresponding center,
allows separating instances of the same semantic class. Moreover, we explore
the effect of incorporating recent successful methods from both segmentation
and detection (i.e. atrous convolution and hypercolumn). Our proposed model is
evaluated on the COCO instance segmentation benchmark and shows comparable
performance with other state-of-art models.Comment: 10 pages including referenc
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
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