KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910212515954688 |
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| author | Lv, Jiaqi Nasir, Esha Sadia Xu, Kesi Jahanifar, Mostafa Chohan, Brinder Singh Elhaminia, Behnaz Raza, Shan E Ahmed |
| author_facet | Lv, Jiaqi Nasir, Esha Sadia Xu, Kesi Jahanifar, Mostafa Chohan, Brinder Singh Elhaminia, Behnaz Raza, Shan E Ahmed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images Lv, Jiaqi Nasir, Esha Sadia Xu, Kesi Jahanifar, Mostafa Chohan, Brinder Singh Elhaminia, Behnaz Raza, Shan E Ahmed Image and Video Processing 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. |
| title | KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2510.23559 |