KongNet通过多头设计实现病理图像中细胞核的精准检测与分类。
KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
- 共享编码器+专用解码器，多任务联合预测细胞核中心、分割掩码和轮廓。
- 在MONKEY、MIDOG等挑战赛中取得第一名，优于现有方法。
- 模型轻量化版适用于实际部署，开源代码助力后续研究。
准确检测与分类组织病理图像中的细胞核对诊断和研究至关重要。我们提出KongNet，一种多头深度学习架构，包含共享编码器和并行的细胞类型特异性解码器。通过多任务学习，每个解码器联合预测细胞核中心点、分割掩码和轮廓，借助空间与通道注意力模块（SCSE）及复合损失函数提升性能。我们在三个大型挑战赛中验证了KongNet的有效性：在MONKEY挑战赛中获第1名（赛道1）和第2名（赛道2）；其轻量级版本KongNet-Det在2025 MIDOG挑战赛中夺冠；在PUMA数据集上经预训练+微调后未作额外优化即进入前三。此外，KongNet在公开的PanNuke和CoNIC数据集上也达到当前最优表现。结果表明，专用多解码器设计在不同组织与染色类型下均具高效性。预训练模型权重与推理代码已开源，以支持未来研究。
原文摘要 · Abstract (English)
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel, cell-type-specialised decoders. Through multi-task learning, each decoder jointly predicts nuclei centroids, segmentation masks, and contours, aided by Spatial and Channel Squeeze-and-Excitation (SCSE) attention modules and a composite loss function. We validate KongNet in three Grand Challenges. The proposed model achieved first place on track 1 and second place on track 2 during the MONKEY Challenge. Its lightweight variant (KongNet-Det) secured first place in the 2025 MIDOG Challenge. KongNet pre-trained on the MONKEY dataset and fine-tuned on the PUMA dataset ranked among the top three in the PUMA Challenge without further optimisation. Furthermore, KongNet established state-of-the-art performance on the publicly available PanNuke and CoNIC datasets. Our results demonstrate that the specialised multi-decoder design is highly effective for nuclei detection and classification across diverse tissue and stain types. The pre-trained model weights along with the inference code have been publicly released to support future research.
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