37 citations · 75 across the 17 of their papers we have counts for
17 papers
Masked conditional variational autoencoders for chromosome straightening
Jingxiong Li, Sunyi Zheng, Zhongyi Shui +9
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists…
Semi-supervised Cell Recognition under Point Supervision
Zhongyi Shui, Yizhi Zhao, Sunyi Zheng +6
Cell recognition is a fundamental task in digital histopathology image analysis. Point-based cell recognition (PCR) methods normally require a vast number of annotations, which is…
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
Yuxuan Sun, Chenglu Zhu, Sunyi Zheng +6
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, of…
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification
Honglin Li, Chenglu Zhu, Yunlong Zhang +5
While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification, such a paradigm still faces performance and generali…
DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection
Zhongyi Shui, Sunyi Zheng, Chenglu Zhu +6
Point-based cell detection (PCD), which pursues high-performance cell sensing under low-cost data annotation, has garnered increased attention in computational pathology community.…
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
Yunlong Zhang, Yuxuan Sun, Honglin Li +3
When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an e…
Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
Shichuan Zhang, Chenglu Zhu, Honglin Li +2
Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal…
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Francesca Palermo, Honglin Li, Alexander Capstick +6
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individu…
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning
Jiatong Cai, Chenglu Zhu, Can Cui +4
Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained imag…
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
Honglin Li, Roonak Rezvani, Magdalena Anita Kolanko +5
Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. One of the key challenges in the management of these condi…
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Honglin Li, Magdalena Anita Kolanko, Shirin Enshaeifar +13
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in lon…
Semi-supervised Federated Learning for Activity Recognition
Yuchen Zhao, Hanyang Liu, Honglin Li +2
Training deep learning models on in-home IoT sensory data is commonly used to recognise human activities. Recently, federated learning systems that use edge devices as clients to s…
Verifying the Causes of Adversarial Examples
Honglin Li, Yifei Fan, Frieder Ganz +2
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs i…
Continual Learning Using Multi-view Task Conditional Neural Networks
Honglin Li, Payam Barnaghi, Shirin Enshaeifar +1
Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known…
Continual Learning Using Bayesian Neural Networks
HongLin Li, Payam Barnaghi, Shirin Enshaeifar +1
Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using…
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
Honglin Li, Shirin Enshaeifar, Frieder Ganz +1
Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning…
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Honglin Li, Frieder Ganz, Shirin Enshaeifar +1
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the prev…