用深度学习精准分割识别宫颈癌细胞，提升早期诊断与风险预测能力。
Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
- 提出多分辨率融合网络，轻量化处理不同尺寸图像。
- 分割准确率90%，交并比达0.83，参数量仅为VGG-19的1/85。
- 支持多任务学习，适合临床辅助诊断与癌症风险评估。
宫颈癌是全球女性第四大常见癌症，需通过巴氏涂片检测早期发现癌前病变以阻止疾病进展。本研究聚焦于细胞边界分割与目标框定位，以分离癌细胞。提出新型深度学习架构——多分辨率融合深度卷积网络，有效处理不同分辨率和长宽比的图像，在SIPaKMeD数据集上表现优异，模型准确率仅比当前最优模型低2%~3%，且仅使用170万可学习参数，约为VGG-19的1/85。此外，引入多任务学习方法，同时完成分割与分类，实现0.83的交并比与90%的分类准确率。最后阶段采用概率方法进行风险评估，提取特征向量预测正常细胞向恶性转化的可能性，可用于宫颈癌预后判断。
原文摘要 · Abstract (English)
Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) architecture, the ``Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios, with its efficacy showcased using the SIPaKMeD dataset. The performance of this DL model was observed to be similar to the state-of-the-art models, with accuracy variations of a mere 2\% to 3\%, achieved using just 1.7 million learnable parameters, which is approximately 85 times less than the VGG-19 model. Furthermore, we introduced a multi-task learning technique that simultaneously performs segmentation and classification tasks and begets an Intersection over Union score of 0.83 and a classification accuracy of 90\%. The final stage of the workflow employs a probabilistic approach for risk assessment, extracting feature vectors to predict the likelihood of normal cells progressing to malignant states, which can be utilized for the prognosis of cervical cancer.
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