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Found 5 papers, 1 papers with code
Cell abundance aware deep learning for cell detection on highly imbalanced pathological data
To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training.
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images
Incorporating cell count loss in the objective function regularizes the network to learn weak gradient boundaries and separate weakly stained cells from background artefacts.
Improving Breast Cancer Detection using Symmetry Information with Deep Learning
At candidate level, AUC value of 0. 933 with 95% confidence interval of [0. 920, 0. 954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0. 929 with [0. 919, 0. 947] confidence interval.
Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation
Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image.
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable
The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly.
