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Sidong Liu

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4 papers

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JBHI Journal 2025 Journal Article

Deep Learning Framework for Classifying Whole-Slide Multiplex Immunofluorescence Images to Predict Immunotherapy Response in Melanoma Patients

Priyanka Rana
Tuba N Gide
Nurudeen A Adegoke
Yizhe Mao
Shlomo Berkovsky
Enrico Coiera
James S Wilmott
Sidong Liu

Immunotherapy has emerged as a prominent approach in melanoma treatment, however a substantial number of patients do not respond effectively. This highlights the critical need to accurately predict immunotherapy responses for designing personalised treatment strategies. Current practices rely predominantly on clinical data and the expertise of oncologists; however, a deeper understanding of molecular interactions through tissue-based biomarkers offers a promising avenue for advancement. Whole-slide multiplex immunofluorescence (mIF) images enable detailed analysis of cells/tissues in their microenvironment, deepening insights into disease mechanisms. However, numerous channels, an extensive image size, and spatially dispersed information of mIF images pose analytical challenges, requiring advanced techniques to effectively learn these intricate features for optimal performance. In this study, we introduce a novel deep-learning framework, Channel Optimisation with Multi-Instance Learning (COMIL), specifically designed to classify whole-slide mIF images for predicting immunotherapy response in melanoma patients. The study demonstrates that a feature extraction method that models inter-channel relationships and captures complex interdependencies among multiple channels of mIF images enhances classification performance. Additionally, incorporating this method within an MIL framework, optimised at both the slide and instance levels, further improves the classification performance of whole-slide mIF images. We evaluated COMIL on mIF images from the Melanoma Institute Australia, where it outperformed baseline methods with substantial improvements on both the internal (AUC increase by 28%) and external test sets (AUC increase by 20%), underscoring its potential for predicting immunotherapy response.

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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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AIIM Journal 2022 Journal Article

Weak label based Bayesian U-Net for optic disc segmentation in fundus images

Hao Xiong
Sidong Liu
Roneel V. Sharan
Enrico Coiera
Shlomo Berkovsky

Fundus images have been widely used in routine examinations of ophthalmic diseases. For some diseases, the pathological changes mainly occur around the optic disc area; therefore, detection and segmentation of the optic disc are critical pre-processing steps in fundus image analysis. Current machine learning based optic disc segmentation methods typically require manual segmentation of the optic disc for the supervised training. However, it is time consuming to annotate pixel-level optic disc masks and inevitably induces inter-subject variance. To address these limitations, we propose a weak label based Bayesian U-Net exploiting Hough transform based annotations to segment optic discs in fundus images. To achieve this, we build a probabilistic graphical model and explore a Bayesian approach with the state-of-the-art U-Net framework. To optimize the model, the expectation-maximization algorithm is used to estimate the optic disc mask and update the weights of the Bayesian U-Net, alternately. Our evaluation demonstrates strong performance of the proposed method compared to both fully- and weakly-supervised baselines.

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YNICL Journal 2018 Journal Article

Evidence of progressive tissue loss in the core of chronic MS lesions: A longitudinal DTI study

Alexander Klistorner
Chenyu Wang
Con Yiannikas
John Parratt
Michael Dwyer
Joshua Barton
Stuart L. Graham
Yuyi You

Objective: Using diffusion tensor imaging (DTI), we examined chronic stable MS lesions, peri-lesional white matter (PLWM) and normal appearing white matter (NAWM) in patients with relapsing-remitting multiple sclerosis (RRMS) for evidence of progressive tissue destruction and evaluated whether diffusivity change is associated with conventional MRI parameters and clinical findings. Method: Pre- and post-gadolinium T1, T2 and DTI images were acquired from 55 consecutive RRMS patients at baseline and 42.3 ± 9.7 months later. Chronic stable T2 lesions of sufficient size were identified in 43 patients (total of 134 lesions). Diffusivity parameters such as axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD) and fractional anisotropy (FA) were compared at baseline and follow-up. MRI was also performed in 20 normal subjects of similar age and gender. Results: = 0.01). Sub-analysis of lesions with lesion-free surrounding revealed the largest MD increase in the lesion core, while MD progression gradually declined towards PLWM. MD in NAWM remained stable over the follow-up period. Conclusion: The significant increase of isotropic water diffusion in the core of chronic stable MS lesions likely reflects gradual, self-sustained tissue destruction in demyelinated white matter that is more aggressive in males.

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