CAJ | 학술논문

病理图像能够揭示疾病的原因及严重程度，在临床诊断中有重要应用。病理图像中局部区域与病理特性之间不明确的对应关系为建立计算机辅助诊断模型带来了困难。基于全局图像特征表达和等分小块等方法难以有效表达病理特性的局部性。提出一种基于多示例多标签学习的活检病理图像自动标注框架，对病理特性的局部性进行表达。通过带区域约束条件的分割算法把病理图像划分为若干视觉上不连续的区域，对区域进行基于纹理和内部结构的特征提取，把病理图像转化为多示例样本，在此基础上提出一种基于贝叶斯学习的多示例多标签稀疏集成算法。在本地大型三甲医院的皮肤科活检样本数据集上进行方法有效性评估，结果表明该方法能得到医学上可接受的标注准确率，从而说明其有效性。
병리도상능구게시질병적원인급엄중정도，재림상진단중유중요응용。병리도상중국부구역여병리특성지간불명학적대응관계위건립계산궤보조진단모형대래료곤난。기우전국도상특정표체화등분소괴등방법난이유효표체병리특성적국부성。제출일충기우다시례다표첨학습적활검병리도상자동표주광가，대병리특성적국부성진행표체。통과대구역약속조건적분할산법파병리도상화분위약간시각상불련속적구역，대구역진행기우문리화내부결구적특정제취，파병리도상전화위다시례양본，재차기출상제출일충기우패협사학습적다시례다표첨희소집성산법。재본지대형삼갑의원적피부과활검양본수거집상진행방법유효성평고，결과표명해방법능득도의학상가접수적표주준학솔，종이설명기유효성。
Histopathological image can reveal the reason and severity of diseases ,w hich is important for clinical diagnosis .Automatic analysis of histopathological image may release doctor’s burden for manual annotation w hich can preserve more time for doctors to focus on special and difficult cases . However , the ambiguous relationship between local regions in a histopathological image and histopathological characteristics makes it difficult to construct a computer‐aid model .An automatic annotation method for histopathological images based on multiple‐instance multiple‐label (MIML ) learning is proposed ,aiming at directly modeling the medical experience of doctors ,w hich suggests that each annotated term associated with an image corresponds to a local visually recognized region . We propose a self‐adaptive region cutting method with constraints ,to segment each image into several visually disjoint regions ,and then perform a feature extraction for each generated region based on texture and inner structures .The whole image is regarded as a bag and regions as instances ,thus an image is expressed as a multiple‐instance sample . Then we propose a sparse ensemble multiple‐instance multiple‐label learning algorithm ,S‐MIMLGP ,based on Bayesian learning ,and compare it with current multiple‐instance single label and multiple‐instance multiple‐label algorithms . The evaluation on a clinical dataset from the dermatology of a large local hospital shows that the proposed method can yield medically acceptable annotation accuracy ,hence indicates its effectiveness .