ACCURATE GASTRIC CANCER SEGMENTATION IN DIGITAL PATHOLOGY IMAGES USING DEFORMABLE CONVOLUTION AND MULTI-SCALE EMBEDDING NETWORKS

Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Automatic gastric cancer segmentation is a challenging problem in digital pathology image analysis.Accurate segmentation of gastric cancer regions can efficiently facilitate clinical diagnosis and pathological research.Technically, this problem suffers from various sizes, vague boundaries, and the non-rigid characters of cancerous regions.For addre

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An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks.For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of cl

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