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1. (WO2018125580) GLAND SEGMENTATION WITH DEEPLY-SUPERVISED MULTI-LEVEL DECONVOLUTION NETWORKS

Pub. No.:    WO/2018/125580    International Application No.:    PCT/US2017/066227
Publication Date: Fri Jul 06 01:59:59 CEST 2018 International Filing Date: Thu Dec 14 00:59:59 CET 2017
IPC: G06N 3/02
G06N 3/04
G06N 3/06
G06N 3/08
A61K 35/22
Applicants: KONICA MINOLTA LABORATORY U.S.A., INC.
Inventors: ZHU, Jingwen
ZHANG, Yongmian
Title: GLAND SEGMENTATION WITH DEEPLY-SUPERVISED MULTI-LEVEL DECONVOLUTION NETWORKS
Abstract:
Pathological analysis needs instance-level labeling on a histologic image with high accurate boundaries required. To this end, embodiments of the present invention provide a deep model that employs the DeepLab basis and the multi-layer deconvolution network basis in a unified model. The model is a deeply supervised network that allows to represent multi-scale and multi-level features. It achieved segmentation on the benchmark dataset at a level of accuracy which is significantly beyond all top ranking methods in the 2015 MICCAI Gland Segmentation Challenge. Moreover, the overall performance of the model surpasses the state-of-the-art Deep Multi-channel Neural Networks published most recently, and the model is structurally much simpler, more computational efficient and weight-lighted to learn.