用触觉手套+深度学习，自动识别乳房肿块位置大小
Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning
- 用柔性触觉手套采集数据，基于InceptionTime模型进行学习
- 对新手用户识别肿块准确率达82.22%，对经验用户超95%
- 适合医疗初学者或日常自检，提升早期发现可能性
乳腺癌是女性首要死因。触诊是早期发现的关键。本文设计了一款基于柔性织物的可穿戴触觉手套，用于在定制硅胶乳房模型（SBPs）中检测肿块。这些模型采用软硅胶模拟人体皮肤和乳腺内部结构，内置直径分别为1.5、1.75和2.0厘米的球形硅胶肿瘤以构建含肿块样本。采用基于InceptionTime架构的深度学习模型，并结合经验与非经验用户间的迁移学习。从10名新手参与者和1名肿瘤科医生处收集数据。结果显示，模型对肿块存在性、大小和位置的分类准确率分别为82.22%、67.08%和62.63%；对未见过的经验用户，准确率分别提升至95.01%、88.54%和82.98%。该技术可辅助非专业人士或医护人员，促进更频繁的常规自检。
原文摘要 · Abstract (English)
Breast cancer is the leading cause of mortality among women. Inspection of breasts by palpation is the key to early detection. We aim to create a wearable tactile glove that could localize the lump in breasts using deep learning (DL). In this work, we present our flexible fabric-based and soft wearable tactile glove for detecting the lumps within custom-made silicone breast prototypes (SBPs). SBPs are made of soft silicone that imitates the human skin and the inner part of the breast. Ball-shaped silicone tumors of 1.5-, 1.75- and 2.0-cm diameters are embedded inside to create another set with lumps. Our approach is based on the InceptionTime DL architecture with transfer learning between experienced and non-experienced users. We collected a dataset from 10 naive participants and one oncologist-mammologist palpating SBPs. We demonstrated that the DL model can classify lump presence, size and location with an accuracy of 82.22%, 67.08% and 62.63%, respectively. In addition, we showed that the model adapted to unseen experienced users with an accuracy of 95.01%, 88.54% and 82.98% for lump presence, size and location classification, respectively. This technology can assist inexperienced users or healthcare providers, thus facilitating more frequent routine checks.
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