SGMCE：厚血涂片疟原虫物种识别的基于片段的形态学概念解释
SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears
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中文总结 AI 辅助
研究旨在解决厚血涂片疟原虫物种识别中深度学习缺乏形态学证据的问题，提出SGMCE框架，无需额外训练等，通过提取特征、查询GPT-4o生成自然语言解释，经多指标验证，在多种检测中取得较好结果。
中文摘要 AI 辅助
疟疾流行地区的疟疾诊断依赖于厚血涂片中疟原虫的物种水平识别，但深度学习检测器在分类检测时未提供预测的形态学证据，限制了显微镜检查人员在病例层面审核这些预测的能力。我们提出了SGMCE（基于片段的形态学概念解释），这是一个无需额外训练、形态学注释和标记解释数据的事后解释框架，它能产生基于厚涂片形态学的每个检测的自然语言解释。对于每个检测，SGMCE提取掩码引导的裁剪缩略图，使用自适应掩码内阈值计算14个手工制作的计算机视觉形态学特征，并根据从世界卫生组织辅助工具汇编的厚涂片特定知识库，用视觉证据和计算测量结果查询GPT-4o。主要输出是一个结构化解释，确定哪些形态学特征支持检测到的物种以及为什么排除竞争物种。通过四个自动指标对解释进行验证：知识库一致性（KBC）、计算机视觉声明忠实性（CCF）、区分度得分（DS）和大语言模型作为评判（LLMj）。一个带有物种感知否定过滤的句子级语义评分规则解决了临床散文和知识库术语之间的词汇不匹配问题。在跨越四种疟原虫物种和白细胞的139张厚涂片图像的737个检测中，寄生虫类的平均KBC为0.91，平均DS为0.99，平均CCF为0.97，而每个规则的CCF细分证实了视觉语言模型基于计算机视觉的声明与它们引用的测量结果一致。
英文摘要
Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without providing morphological evidence for their predictions, limiting the ability of microscopists to audit those predictions at the case level. We present SGMCE (Segment-Grounded Morphological Concept Explanation), a post-hoc explanation framework that requires no additional training, no morphological annotations, and no labelled explanation data, yet produces per-detection natural-language explanations anchored in thick-smear morphology. For each detection, SGMCE extracts mask-guided crop thumbnails, computes fourteen handcrafted computer-vision morphological features (shape, colour, chromatin, haemozoin pigment) using adaptive within-mask thresholds, and queries GPT-4o with both visual evidence and computed measurements, conditioned on a thick-smear-specific knowledge base compiled from the World Health Organization bench aids. The primary output is a structured explanation identifying which morphological features support the detected species and why the competing species are excluded. Explanations are validated by four automatic metrics: Knowledge-Base Consistency (KBC), CV-Claim Faithfulness (CCF), Discriminativeness Score (DS), and LLM-as-Judge (LLMj). A sentence-level semantic scoring rule with species-aware negation filtering resolves the vocabulary mismatch between clinical prose and knowledge-base terms. Across 737 detections from 139 thick-smear images spanning four Plasmodium species and white blood cells, parasite-class mean KBC is 0.91, mean DS is 0.99, and mean CCF is 0.97, while a per-rule CCF breakdown confirms that the CV-grounded claims made by the vision-language model are consistent with the measurements they cite.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
