深度學習促進醫學圖像分析 (ART/245CP)

深度學習促進醫學圖像分析 (ART/245CP)

  • 深度學習促進醫學圖像分析 (ART/245CP)
    ART/245CP
    平台
    01 / 03 / 2018 - 31 / 08 / 2019
    11,694

    吳曉華博士

    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.

    江蘇康克移軟軟件有限公司
    華亘致遠(香港)有限公司


    隨著每天為處理大量臨床診斷生成的醫學圖像數據,醫學圖像分析非常需要自動計算機輔助運算。由於缺乏放射科醫師和病理學家的資源,工作量的快速增加,成像的難度提高,問題進一步複雜化。在學術界和業界已開展了大量研究,試圖滿足臨床使用的高標準要求。然而,現有的解決方案尚未在臨床階段廣泛採用。另一方面,由於深度學習在許多領域都有貫徹性和有較佳的性能表現,這算法在機器學習界中引起極大關注。深度學習應用程序的成功關鍵在於使用大量訓練數據,但條件應用在醫學圖像分析領域中,通常是屬於奢侈的安排。 本提案的目標是要促進計算機輔助診斷在醫學領域的發展。我們提出了打破醫學領域“缺乏訓練數據”瓶頸的方法,輸出最先進的深度學習技術。我們提出一種用於一般訓練準備的半監督圖像註釋框架;建立醫學圖像分析的深度學習模型指南。我們將重點開發兩種深度學習支持的計算機輔助診斷解決方案,應用於數字病理學和視頻內窺鏡檢查,這將對比當前手動診斷方式,更能提高相關的靈敏度。