1. Reference design of generalized training data preparation for the two specific medical image analytics applications: (a) Development of Deep Learning model with public data for annotation extraction; (b) Development of a user-friendly interface for annotation results review. 2. Reference design of building state-of-the-art Deep Learning model for the two specific medical image analytics applications: (a) Valuation of new annotations generated in 1(a); (b) Development of new Deep Learning models with new large-scale training data; (c) General guideline for Deep Learning model building. 3. Two Deep Learning Computer-Aided Diagnosis application solutions: (a) Digital pathology image analysis using Deep Learning; (b) Anomaly detection in capsule endoscopy video. 4. Contract Service Deliverables for Customer 1: (a) Image quality examination algorithm with source codes for capsule endoscopy video; (b) Gastrointestinal tract division algorithm with source codes for capsule endoscopy video; (c) Spectral image enhancement algorithm with source codes for virtual chromoendoscopy; (d) Redundant image content detection algorithm with source codes for consecutive frames; (e) Gastrointestinal abnormality detection algorithm with source codes for capsule endoscopy video.
江苏康克移软软件有限公司
华亘致远(香港)有限公司
隨著每天为处理大量临床诊断生成的医学图像数据，医学图像分析非常需要自动计算机辅助运算。由於缺乏放射科医师和病理学家的资源，工作量的快速增加，成像的难度提高，问题进一步复杂化。在学术界和业界已开展了大量研究，试图满足临床使用的高標准要求。然而，现有的解决方案尚未在临床阶段广泛採用。另一方面，由於深度学习在许多领域都有贯彻性和有较佳的性能表现，这算法在机器学习界中引起极大关注。深度学习应用程序的成功关键在於使用大量训练数据，但条件应用在医学图像分析领域中，通常是属於奢侈的安排。 本提案的目標是要促进计算机辅助诊断在医学领域的发展。我们提出了打破医学领域“缺乏训练数据”瓶颈的方法，输出最先进的深度学习技术。我们提出一种用於一般训练准备的半监督图像註释框架；建立医学图像分析的深度学习模型指南。我们將重点开发两种深度学习支持的计算机辅助诊断解决方案，应用於数字病理学和视频內窥镜检查，这將对比当前手动诊断方式，更能提高相关的灵敏度。