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Found 4 papers, 0 papers with code
G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection
By comparing the performance between the original Faster R-CNN and that with partially separated feature maps, we show that: (1) Sharing high-level features for the classification and localization tasks is sub-optimal; (2) Large stride is beneficial for classification but harmful for localization; (3) Global context information could improve the performance of classification.
DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge
Previous methods only classify manually segmented single chromosome, which is far from clinical practice.
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy
Based on these principles, we designed a novel boundary refinement architecture to improve localization accuracy by combining coarse-to-fine framework with feature pyramid structure, named as Pyramidal Bounding Box Refinement network(PBRnet), which parameterizes gradually focused boundary areas of objects and leverages lower-level feature maps to extract finer local information when refining the predicted bounding boxes.
DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis.
