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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.
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography
It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists.
DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis.
