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Haobo Chen

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4 papers

2 author rows

AIIM Journal 2026 Journal Article

Weakly-supervised ultrasound image segmentation with elliptical shape prior constraint

Changyan Wang
Yehua Cai
Ruyi Yang
Haobo Chen
Jiang Shang
Hong Ding
Qi Zhang

Accurate pixel-level segmentation of ultrasound (US) images is vital for computer-aided disease screening, diagnosis, and treatment response evaluation. The weakly supervised methods have the potential to reduce the time-consuming and labor-intensive workload for radiologists, paving the way for further automation in the quantitative analysis of US images. Among these methods, the multiple instance learning (MIL) has proven effective and is often applied to prediction tasks with insufficiently labeled data. In US examinations, the elliptical region formed by intersecting lines used by radiologists for target annotation serves as a crucial prior information. Therefore, we propose a novel weakly supervised method called elliptical shape prior constraint MIL (ESPC-MIL) for pixel-level segmentation of US images. ESPC-MIL incorporates an elliptical shape prior constraint into the MIL framework, delivering more accurate foreground and background candidate regions for MIL, which enhances its predictive performance for tissues and organs with approximately elliptical shapes. Furthermore, the method utilizes elliptical shape prior information for global supervision, improving edge segmentation and localization accuracy. Compared to other weakly supervised methods, ESPC-MIL achieves state-of-the-art results on four US image datasets: Achilles tendon dataset, median nerve dataset, private breast tumor dataset, and public breast ultrasound image dataset, with Dice similarity coefficients of 0. 855, 0. 849, 0. 876, and 0. 748, respectively. It demonstrates performance comparable to fully supervised segmentation methods while significantly reducing annotation requirements. Notably, the method demonstrates a more significant performance improvement in segmenting objects with approximately elliptical shapes compared to those with complex shapes. Source codes and models are available at https: //github. com/CYWang-kayla/ESPC-MIL-Model.

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ICML Conference 2025 Conference Paper

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective

Firas Laakom
Haobo Chen
Jürgen Schmidhuber
Yuheng Bu

Despite substantial progress in promoting fairness in high-stake applications using machine learning models, existing methods often modify the training process, such as through regularizers or other interventions, but lack formal guarantees that fairness achieved during training will generalize to unseen data. Although overfitting with respect to prediction performance has been extensively studied, overfitting in terms of fairness loss has received far less attention. This paper proposes a theoretical framework for analyzing fairness generalization error through an information-theoretic lens. Our novel bounding technique is based on Efron–Stein inequality, which allows us to derive tight information-theoretic fairness generalization bounds with both Mutual Information (MI) and Conditional Mutual Information (CMI). Our empirical results validate the tightness and practical relevance of these bounds across diverse fairness-aware learning algorithms. Our framework offers valuable insights to guide the design of algorithms improving fairness generalization.

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JBHI Journal 2023 Journal Article

CCT-Unet: A U-Shaped Network Based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI

Yifei Yan
Rongzong Liu
Haobo Chen
Limin Zhang
Qi Zhang

The accurate segmentation of prostate region in magnetic resonance imaging (MRI) can provide reliable basis for artificially intelligent diagnosis of prostate cancer. Transformer-based models have been increasingly used in image analysis due to their ability to acquire long-term global contextual features. Although Transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate MRI due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (CNN) could retain these local features well. Therefore, a robust prostate segmentation model that can aggregate the characteristics of CNN and Transformer is desired. In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection. Extensive experiments have been conducted to compare our CCT-Unet with several state-of-the-art methods on both the ProstateX open dataset and the self-bulit Huashan dataset, and the results have consistently shown the accuracy and robustness of our CCT-Unet in MRI prostate segmentation.

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YNICL Journal 2016 Journal Article

Lesion-symptom mapping of a complex figure copy task: A large-scale PCA study of the BCoS trial

Haobo Chen
Xiaoping Pan
Johnny King Lam Lau
Wai-Ling Bickerton
Boddana Pradeep
Maliheh Taheri
Glyn Humphreys
Pia Rotshtein

Complex figure copying is a commonly used neuropsychological test. Here we explored the neural basis of the factors underlying complex figure copying (CFC), using data from the Birmingham Cognitive Screen (BCoS) in a large group of sub-acute, ischemic stroke patients (239). We computed two analyses: in the first we assessed the contribution of co-morbid deficits (i.e. in gesture processing, object use, visual neglect, pictures naming and sustained attention) to the lesions associated with CFC. In a second analysis a Principle Component Analysis (PCA) was used to isolate different underlying task components and to link to clinical neuroimaging scans. A voxel-based morphometry (VBM) analysis showed that poor CFC performance was associated with lesions to bi-lateral thalamus, lingual, right fusiform and right inferior parietal cortices (rIPC). The latter association with the posterior parietal cortex was diminished after controlling for neglect. Follow up analysis showed the neglect partially mediated the correlation of CFC and rIPC. The PCA revealed three main underlying components: (1) a component associated with high-level motor control common to different measures of apraxia and linked to the left postcentral gyrus, the right thalamus and middle frontal gyrus; (2) a visuo-motor transformation component unique to the CFC and associated with lesions to the posterior occipital and sensory cortices; (3) a component associated with multistep object use tasks which was correlated with lesions to the left inferior frontal orbital gyrus, the right fusiform and cerebellum. Using clinical symptoms, cognitive profiles and lesion mapping we showed that beyond visual perception, CFC performance is supported by three functional networks: one for high-level motor control, a visuo-motor transformation component, and multistep object use network.

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