DeNuC: Decoupling Nuclei Detection and Classification in Histopathology

Zijiang Yang; Chen Kuang; Dongmei Fu

cs.CVMar 4, 2026

DeNuC: Decoupling Nuclei Detection and Classification in Histopathology

Authors: Zijiang Yang, Chen Kuang, Dongmei Fu

Organizations: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China · Beijing Engineering Research Center of Industrial Spectrum Imaging

Abstract

Pathology Foundation Models (FMs) have shown strong performance across a wide range of pathology image representation and diagnostic tasks. However, FMs do not exhibit the expected performance advantage over traditional specialized models in Nuclei Detection and Classification (NDC). In this work, we reveal that jointly optimizing nuclei detection and classification leads to severe representation degradation in FMs. Moreover, we identify that the substantial intrinsic disparity in task difficulty between nuclei detection and nuclei classification renders joint NDC optimization unnecessarily computationally burdensome for the detection stage. To address these challenges, we propose DeNuC, a simple yet effective method designed to break through existing bottlenecks by Decoupling Nuclei detection and Classification. DeNuC employs a lightweight model for accurate nuclei localization, subsequently leveraging a pathology FM to encode input images and query nucleus-specific features based on the detected coordinates for classification. Extensive experiments on three widely used benchmarks demonstrate that DeNuC effectively unlocks the representational potential of FMs for NDC and significantly outperforms state-of-the-art methods. Notably, DeNuC improves F1 scores by 4.2% and 3.6% (or higher) on the BRCAM2C and PUMA datasets, respectively, while using only 16% (or fewer) trainable parameters compared to other methods. Code is available at https://github.com/ZijiangY1116/DeNuC.

Figures & tables

Figure 1: Analysis of model performance during the NDC training. (a) We evaluate the representation capability of UNI2-H [ 2 ] during joint optimization for NDC on the OCELOT [ 13 ] dataset via linear probing. UNI2-H undergoes a severe representation degradation in the early training phase. Although the performance subsequently recovers, it fails to regain its initial optimal level and rapidly deteriorates into over-fitting. (b) We evaluate the detection and classification F1 scores of a ConvNeXt-S [ 8 ] model pre-trained solely on ImageNet-1K throughout the training process on PUMA [ 14 ] . Despite lacking domain-specific pathology pre-training, nuclei detection converges approximately 2.3

\times

faster than classification, highlighting the inherent difficulty disparity between the two tasks.

Figure 2: Comparison of DeNuC and SOTA methods. DeNuC achieves significantly superior performance across three benchmark datasets.

Figure 3: Illustration of (a) Joint optimization and (b) Decoupled optimization for NDC. Existing methods require the backbone to accommodate additional optimization objectives beyond nuclei representation, leading to representation degradation. In contrast, DeNuC employs an independent lightweight detection network

\mathcal{D}

for nuclei localization, thereby allowing the backbone to focus exclusively on representation learning.

Method	Training	BRCAM2C				OCELOT			PUMA
Method	Params.	$F^{Lym.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$	$F^{Lym.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$
CGT [ 9 ]	37M	56.42	75.98	50.44	60.95	68.77	61.30	65.03	76.55	79.66	54.20	70.14
SENC [ 10 ]	27M	57.94	76.50	49.42	61.29	70.02	62.08	66.05	77.38	81.51	54.38	71.09
CellViT [ 6 ]	143M	67.20	78.20	51.81	65.73	67.36	60.22	63.79	79.07	81.16	57.96	72.73
Hover-Net [ 4 ]	34M	62.31	75.25	49.58	62.38	69.27	61.03	65.15	75.72	78.36	49.53	67.87
DPA-P2PNet [ 17 ]	32M	59.65	77.26	55.26	64.06	70.07	59.92	64.99	76.80	81.87	54.04	70.90

Table 1: Comparison of nucleus detection and classification in F1-score % (

\uparrow

F^{Tum.}

F^{Lym.}

F^{Oth.}

, and

F^{Avg.}

denote the F1 score of tumor nucleus, lymphocytes, other nucleus, and average, respectively. The best results are highlighted in bold , and the second-best results are in underlined . DeNuC significantly outperforms other methods.

Backbone	Params.	BRCAM2C		OCELOT		PUMA
Backbone	Params.	$F^{Det.}$	$F^{Avg.}$	$F^{Det.}$	$F^{Avg.}$	$F^{Det.}$	$F^{Avg.}$
SN (0.5 $\times$ )	0.3M	86.61	71.43	79.89	68.85	92.23	75.45
SN (1.0 $\times$ )	1.0M	86.98	71.58	80.97	69.74	93.13	75.98
SN (1.5 $\times$ )	2.7M	87.00	71.90	81.07	69.76	93.28	76.19
SN (2.0 $\times$ )	4.3M	87.52	71.97	81.33	69.94	93.57	76.36
ResNet-50	26M	87.29	71.90	81.48	70.03	93.22	76.07

Table 2: Ablation study of

\mathcal{D}

in F1-score % (

\uparrow

). (Left) Comparison of different backbones. (Right) Ablation of cross-dataset training strategy.

N_{nu}

denotes the number of nuclei in the training set.

F^{Det.}

denotes the F1 score of detection. "SN" denotes ShuffleNetV2. "Separated" and "Joint" indicate that models are trained individually on each dataset and jointly across all datasets, respectively.

Method	$P_{det}$	Training	BRCAM2C				OCELOT			PUMA
Method	$P_{det}$	Params.	$F^{Lym.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$	$F^{Lym.}$	$F^{Tum.}$	$F^{Oth.}$	$F^{Avg.}$
Linear	✓	4.6K	69.73	85.10	61.08	71.97	73.83	66.04	69.94	81.00	85.25	62.85	76.37
Lora	✓	2.4M	71.79	85.33	61.40	72.84	74.19	66.93	70.56	81.23	85.70	63.38	76.77
Fully	✓	681M	71.03	85.50	61.11	72.55	73.87	65.52	69.69	81.45	85.37	63.13	76.65
End-to-end	✗	686M	68.21	79.42	56.67	68.10	67.37	58.15	62.76	79.50	83.19	62.20	74.97

Table 3: Ablation study on the optimization strategy of the classification network in F1-score % (

\uparrow

). Providing nuclei coordinates highly improves performance.

Explore similar work

NuNext: Reframing Nucleus Detection as Next-Point Detection
Mar 7, 2026Zhongyi Shui, Honglin Li, Xiaozhong Ji +7Computational Pathology Object Detection
Zhejiang University · Westlake University · Shanghai Artificial Intelligence Laboratory +3
AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
May 31, 2026Spoorthi M, Suja PalaniswamyHistopathology Image Segmentation Hybrid CNN-Transformer Architectures
Department of Computer Science & Engineering, Amrita School of Computing, Bengaluru, Amrita Vishwa Vidyapeetham, India
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Jul 30, 2026Sweta Banerjee, Alireza Teimoury, Nils Porsche +11Computational Pathology Pathology Foundation Models
Flensburg University of Applied Sciences, Flensburg, Germany · Julius-Maximilians-Universität Würzburg, Würzburg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany +4

Mar 7, 2026J/K move · Enter open · S save

NuNext: Reframing Nucleus Detection as Next-Point Detection

Zhongyi Shui, Honglin Li, Xiaozhong Ji, Ye Zhang+6

Zhejiang University · Westlake University · Shanghai Artificial Intelligence Laboratory+3

Computational Pathology Object Detection RL Fine-Tuning

Open paper AskPDF arXiv

Nucleus detection in histopathology is pivotal for a wide range of clinical applications. Existing approaches either regress nuclear proxy maps that require complex post-processing, or employ dense anchors or queries that introduce severe foreground-background imbalance. In this work, we reformulate nucleus detection as next-point prediction, wherein a multimodal large language model is developed to directly output foreground nucleus centroids from the input image. The model is trained in two stages. In the supervised learning stage, we propose spatial-aware soft supervision to relax strict centroid matching and a chain-of-visual-thought strategy to incorporate visual priors that facilitate coordinate prediction. In the reinforcement fine-tuning stage, we design distribution matching reward, low-variance group filtering, and fine-grained advantage shaping to further improve the model's detection quality. Extensive experiments on nine widely used benchmarks demonstrate the superiority of our method. Code will be released soon.

Abstract

Explore similar work

NuNext: Reframing Nucleus Detection as Next-Point Detection

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures