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Fang Hao

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3 papers

2 author rows

EAAI Journal 2024 Journal Article

Multi-scale multi-instance contrastive learning for whole slide image classification

Jianan Zhang
Fang Hao
Xueyu Liu
Shupei Yao
Yongfei Wu
Ming Li
Wen Zheng

Multi-instance learning (MIL) has become the mainstream solution for processing super-high resolution whole slide images (WSIs) with the pyramidal structure in digital pathology. Current MIL-based methods usually learn features from WSI at a specific magnification, ignoring the multi-scale information contained in the WSI and the comparative learning of global features. In addition, the lack of instance labeling can lead to weak model supervision, which may compromise the model’s ability to discriminate fine-grained features, ultimately affecting bag-level feature learning. Therefore, we propose a novel multi-scale multi-instance contrastive learning framework to learn more discriminative feature representation across scales for pathological WSI classification. The proposed method begins with a two-stream feature aggregator module, which extracts both bag embeddings and selects the representative instances simultaneously. Following the bag embedding branch, a multi-scale contrastive learning module is designed to learn the global feature comparisons of WSIs across multiple scales by leveraging its inherent pyramid structure. Additionally, based on the instances selection branch, a patch-level classifier is combined with the bag-level classifier to jointly optimize the model training process, enhancing the supervision of the model. The proposed framework is evaluated on three publicly available WSI datasets, achieving an area under the curve of 95. 8%, 95. 5%, and 88. 2%, respectively, consistently outperforming all the compared methods including single- and multi-scale ones.

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EAAI Journal 2023 Journal Article

Ada-CCFNet: Classification of multimodal direct immunofluorescence images for membranous nephropathy via adaptive weighted confidence calibration fusion network

Ruili Wang
Xueyu Liu
Fang Hao
Xing Chen
Xinyu Li
Chen Wang
Dan Niu
Ming Li

In the pathological diagnosis of early, late and non-membranous nephropathy, direct immunofluorescence is highly likely to present potentially specific lesions, while it is often overlooked due to the difficulty of screening with naked eyes. With the advanced progress of deep learning, they have shown powerful abilities in detecting potential lesions. In this paper, we propose an adaptive weighted confidence calibration fusion framework (Ada-CCFNet) consisting of a preprocessing module, an adaptive weighted confidence calibration fusion (Ada-CCF) module and a classification module for diagnosis of membranous nephropathy by classifying the multimodal direct immunofluorescence images. In the preprocessing module, we use the well-known U-Net to segment individual glomeruli and standardize their luminance appearance by the average luminance difference method, allowing the subsequent modules to focus more on the diseased glomerular region. Subsequently, in the Ada-CCF module, six confidence calibration methods are utilized for two main direct immunofluorescence images, IgG and C3, and the comprehensive calibration scores are obtained based on the adaptive weighted fusion of six confidence calibration methods to obtain more reliable confidence level, in which the adaptive weights are related with expected calibration error reductions. For the classification module, the weighted probability scores of IgG and C3 are jointly fed into the module to achieve the classification by random forest. Experimental results showed that Ada-CCFNet achieves the classification accuracy of 73. 52%, surpassing the methods of using single IgG or C3 images and positive grade indicator with 8. 24%, 8. 94% and 22. 76%, and outperforming the compared methods in the classification of membranous nephropathy.

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IROS Conference 2009 Conference Paper

Sliding angle reconstruction and robust lateral control of autonomous vehicles in presence of lateral disturbance

Fang Hao
LiHua Dou
Jie Chen 0003

In this paper the problem of path following control of autonomous vehicles subject to sliding is addressed. First a kinematic model is built which takes sliding effects into account by introducing two additional tire sliding angles. Since the tire sliding angles cannot be directly measured by sensors, an adaptive robust Luenberger observer is designed. With this observer, the tire cornering stiffness instead of the sliding angles is identified in presence of time-varying lateral disturbance. The Lyapunov stability theory guarantees that the estimated cornering stiffness would converge to a neighborhood of the real value when control inputs excitated the system persistently. But due to the existence of the lateral disturbance which causes loss of accuracy of the sliding angle reconstruction, the previously designed anti-sliding controller whose effectiveness completely depends on the estimation of the sliding angles cannot yield satisfactory results. To overcome this problem a tire-oriented kinematic model is built in which the inaccuracy of the sliding angle reconstruction is modeled in form of additive disturbances to the kinematic model. By transforming the tire-oriented kinematic model into a perturbed chained system, a sliding mode controller, which is robust to both the sliding effects and the negative effects of the lateral disturbance is designed with the help of the natural algebraic structure of the chained systems. Simulation results show that the proposed methods can provide accurate estimation of the sliding angles and guarantee high anti-sliding control accuracy even in presence of time-varying lateral disturbance.

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