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Cong Cong

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3 papers

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JBHI Journal 2025 Journal Article

ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound images

Linkuan Zhou
Zhexin Chen
Yufei Shen
Junlin Xu
Ping Xuan
Yixin Zhu
Yuqi Fang
Cong Cong

Automated segmentation of the fetal head in ultrasound images is critical for prenatal monitoring. How-ever, achieving robust segmentation remains challenging due to the poor quality of ultrasound images and the lack of annotated data. Semi-supervised methods alleviate the lack of annotated data but struggle with the unique characteristics of fetal head ultrasound images, making it challenging to generate reliable pseudo-labels and enforce effective consistency regularization constraints. To address this issue, we propose a novel semi-supervised framework, ERSR, for fetal head ultrasound segmentation. Our framework consists of the dual-scoring adaptive filtering strategy, the ellipse-constrained pseudo-label refinement, and the symmetry-based multiple consistency regularization. The dual-scoring adaptive filtering strategy uses boundary consistency and contour regularity criteria to evaluate and filter teacher outputs. The ellipse-constrained pseudo-label refinement refines these filtered outputs by fitting leastsquares ellipses, which strengthens pixels near the center of the fitted ellipse and suppresses noise simultaneously. The symmetry-based multiple consistency regularization enforces multi-level consistency across perturbed images, symmetric regions, and between original predictions and pseudo-labels, enabling the model to capture robust and stable shape representations. Our method achieves stateof-the-art performance on two benchmarks. On the HC18 dataset, it reaches Dice scores of 92. 05% and 95. 36% with 10% and 20% labeled data, respectively. On the PSFH dataset, the scores are 91. 68% and 93. 70% under the same settings.

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AAAI Conference 2024 Conference Paper

Decoupled Optimisation for Long-Tailed Visual Recognition

Cong Cong
Shiyu Xuan
Sidong Liu
Shiliang Zhang
Maurice Pagnucco
Yang Song

When training on a long-tailed dataset, conventional learning algorithms tend to exhibit a bias towards classes with a larger sample size. Our investigation has revealed that this biased learning tendency originates from the model parameters, which are trained to disproportionately contribute to the classes characterised by their sample size (e.g., many, medium, and few classes). To balance the overall parameter contribution across all classes, we investigate the importance of each model parameter to the learning of different class groups, and propose a multistage parameter Decouple and Optimisation (DO) framework that decouples parameters into different groups with each group learning a specific portion of classes. To optimise the parameter learning, we apply different training objectives with a collaborative optimisation step to learn complementary information about each class group. Extensive experiments on long-tailed datasets, including CIFAR100, Places-LT, ImageNet-LT, and iNaturaList 2018, show that our framework achieves competitive performance compared to the state-of-the-art.

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AAMAS Conference 2021 Conference Paper

Intrinsic Motivated Multi-Agent Communication

Chuxiong Sun
Bo Wu
Rui Wang
Xiaohui Hu
Xiaoya Yang
Cong Cong

Efficient communication is a promising way to achieve cooperation among agents in many real-world scenarios. However, aimless and motiveless information sharing may not work or even degrade the cooperative performance. Typically, the multi-agent communication behaviors are motivated by extrinsic rewards from environment. We conclude the mechanism as ’Communicate what rewards you’. In this work, we present a novel communication mechanism called Intrinsic Motivated Multi-Agent Communication (IMMAC). Our key insight can be summarized as ’Communicate what surprises you’. Concretely, we use an observation-dependent intrinsic value to represent the importance of observed information. Then a gating mechanism and an attentional mechanism based on intrinsic values are designed to control communication. By encouraging agent to communicate and focus on the observations with uncertain and important information, our algorithm achieves superior communication efficiency and cooperative performance. We evaluate IMMAC on a variety of challenging tasks, and demonstrate that intrinsic values are sufficient to drive efficient communication behaviors. Moreover, we found that the combination of intrinsic values and extrinsic values can further improve the communication efficiency. Consequently, intrinsic motivation is a promising way to control communication and it is capable of being a good complement to the existing extrinsic motivated communication methods.

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