The advent of Whole Slide Imaging (WSI) has revolutionised digital pathology by enabling computational analysis of gigapixel-scale images. To handle their large size, most deep learning models divide WSIs into patches and apply Multiple Instance Learning (MIL) for slide-level classification. However, MIL models often depend on pre-trained feature extractors, resulting in domain gaps between natural and pathological images. Parameter-Efficient Fine-Tuning (PEFT) via visual prompting has emerged to bridge this gap with minimal overhead. Nevertheless, existing visual prompts are typically attached at the image level and tightly coupled with specific architectures such as CNNs or ViTs, limiting generalisability and scalability in WSI tasks. To overcome these limitations, we propose Slide-aware Deep Feature Prompt (S-DFP), a novel visual prompting method which derives task-specific information directly from feature embeddings and is initialised with slide-specific cues, thereby enhancing compatibility with diverse feature extractors and MIL frameworks. Experiments on four benchmark datasets, CAMELYON16, BRIGHT, TCGA-IDH, and UniToPath, demonstrate that S-DFP consistently boosts MIL model performance by 2–5% in AUC while introducing less than 0.02% additional parameters. Furthermore, when integrated with recent pathology foundation models, S-DFP yields additional performance gains. The code is publicly available at S-DFP .

Atopic dermatitis (AD) is a chronic inflammatory skin disorder characterized by recurrent itching, erythema, dryness, and eczematous lesions. Automated AD severity assessment is crucial for cost-effective and precision clinical decision-making but remains challenging. This is due to the subtle contrast variations between key dermatological signs and significant variations in lesion sizes across patients and disease stages. To address these issues, we propose ADSA-Net, which is designed to handle both intra- and inter-class variabilities. ADSA-Net first extracts multi-scale texture-aware features to effectively model variations in lesion size and texture. It then leverages contrastive learning to enhance intra- and inter-class differentiation, strengthening model's discriminatory ability for samples that are difficult to distinguish. Finally, ADSA-Net refines the learning process by leveraging a dynamic feature pool of correctly classified samples to guide the calibration of misclassified instances, enhancing overall accuracy. We further establish a dataset for AD severity assessment. Comprehensive experiments on this dataset show that ADSA-Net significantly outperforms existing state-of-the-art methods.

Universal, transferable whole-slide image (WSI) representations are central to computational pathology. Incorporating multiple markers (e.g., immunohistochemistry, IHC) alongside H&E enriches H&E-based features with diverse, biologically meaningful information. However, progress is limited by the scarcity of well-aligned multi-stain datasets. Inter-stain Misalignment shifts corresponding tissue across slides, hindering consistent patch-level features and degrading slide-level embeddings. To address this, we curated a slide-level aligned, five-stain dataset (H&E, HER2, KI67, ER, PGR) to enable paired H&E-IHC learning and robust cross-stain representation. Leveraging this dataset, we propose Cross-Stain Contrastive Learning (CSCL), a two-stage pretraining framework: a lightweight adapter trained with patch-wise contrastive alignment to improve the compatibility of H&E features with corresponding IHC-derived contextual cues; and slide-level representation learning with Multiple Instance Learning (MIL), which uses a cross-stain attention fusion module to integrate stain-specific patch features and a crossstain global alignment module to enforce consistency among slide-level embeddings across different stains. Experiments on cancer subtype classification, IHC biomarker status classification, and survival prediction, show consistent gains by yielding high-quality, transferable H&E slide-level representations. The code and data are available at: https://github.com/lily-zyz/CSCL.

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.

Cell nuclei segmentation in histopathology images is critical in computer-aided diagnosis and treatment planning. However, this task is challenging due to inherent heterogeneity in histopathology images especially when originating from different domains, caused by variations in imaging protocols, staining techniques, and tissue preparation methods. Such domain shifts can significantly affect segmentation performance when the segmentation model is trained and tested on different domains. In this work, we present a novel gradient-based meta-learning approach for domain generalisation in histopathology cell nuclei segmentation. Specifically, we propose a domain-aware regularisation to correct each pixel’s classification based on the specific domain. We also embed a novel network module to preserve the colour features in histopathology images via an enhanced feature extraction procedure. We demonstrate that our proposed framework can achieve consistent and accurate segmentation performance across domains through extensive experiments on multiple histopathology datasets from diverse sources. Our code is available at: https://github.com/winnie172026/DG.

Medical image datasets are often imbalanced due to biases in data collection and limitations in acquiring data for rare conditions. Addressing class imbalance is crucial for developing reliable deep-learning algorithms capable of effectively handling all classes. Recent class imbalanced methods have investigated the effectiveness of self-supervised learning (SSL) and demonstrated that such learned features offer increased resilience to class imbalance issues and obtain much improved performances over other types of class imbalanced methods. However, existing SSL methods either lack end-to-end capabilities or require substantial memory resources, potentially resulting in sub-optimal features and classifiers and limiting their practical usage. Moreover, the conventional pooling operations (e.g., max-pooling, or average-pooling) tend to generate less discriminative features when datasets pose high inter-class similarities. To alleviate the above issues, in this study, we present a novel end-to-end self-supervised learning framework tailored for imbalanced medical image datasets. Our framework constitutes an adaptive contrastive loss that can dynamically adjust the model’s learning focus between feature learning and classifier learning and a feature aggregation mechanism based on Graph Neural Networks to further enhance feature discriminability. We evaluate the effectiveness of our framework on four medical datasets, and the experimental results highlight its superior performance in imbalanced image classification tasks.

In this paper, we present a video analysis method that automatically detects crowds violating social distancing regulations in public spaces, which is widely accepted to be essential to minimise the spreading of COVID-19. While various approaches have been published online to tackle this problem, our work presents a systematic study with comprehensive quantitative analysis of different deep learning models on multiple datasets. We experimented with two types of one-stage pedestrian detection models and further optimised their performance with a repulsion loss to address occlusions in crowds. We also propose a distance computation technique with locally adaptive threshold to approximate the actual spatial distance between pedestrians in the real world. In addition, since there is no existing dataset providing ground truth annotations of distances, we manually annotated three public datasets with such information to perform quantitative evaluation of our crowd detection method. Our comprehensive evaluation shows that our method achieves good detection performance with improvement provided by repulsion loss. Our code and ground truth annotations can be obtained from https://github.com/thomascong121/SocialDistance.