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Peer-Reviewed Publication
Med Image Anal2026;114104233.July 25, 2026Journal Article

KongNet: A multi-headed deep learning model for detection and classification of nuclei in histopathology images.

Jiaqi Lv1, Esha Sadia Nasir2, Kesi Xu3, Mostafa Jahanifar3, Brinder Singh Chohan4, Behnaz Elhaminia2, Shan E Ahmed Raza2
1VISION Lab, Department of Computer Science, University of Warwick, Coventry, United Kingdom; Tissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom. Electronic address: jiaqi.lv@warwick.ac.uk.
2VISION Lab, Department of Computer Science, University of Warwick, Coventry, United Kingdom; Tissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom.
3Tissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom.
4Department of Cellular Pathology, University Hospitals of Derby and Burton NHS Foundation Trust, United Kingdom.
PubMed DOI

Abstract

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 Chann…

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