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 .

Since the inception of BraTS challenge, a series of methods has been developed for brain tumor segmentation over the past years. Although these methods achieved promising results, they mostly focus on glioma segmentation, largely due to their relatively high incidence. These fully-supervised methods may not be applicable as they rely on abundant labeled data, which is intrinsically inaccessible for rarer types of brain tumors. Data-efficient transfer learning approaches like few-shot learning and domain adaptation assume full access to source data, which may not be feasible in real-life scenarios due to privacy and confidentiality concerns. In this work, we propose a new source-free few-shot learning framework for rarer brain tumor segmentation that adapts source model trained on gliomas to other less common brain tumors such as meningioma, metastasis and pediatric tumors with only a few labeled target data. The proposed framework follows a dual-branch prototypes learning structure that harmonize preservation of common knowledge from source class and learning new features from target. We show that our method gains a 6% increase in Dice score over representative source-free domain adaptation methods, and achieves comparable performance against its fully-supervised counterpart.

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.

We present a fractal-based methodology to analyze brain magnetic resonance images (MRI) for the automated detection of cerebral arteriovenous malformations (AVM). First, the MRI is split into right and left hemispheres components whose fractal dimensions (FD) are estimated using detrended fluctuation analysis (DFA). Then, the obtained FD values are used to characterize healthy and AVM-affected brain MRIs. Using a database of twenty-eight images, and ten-fold cross validation, classification by a support vector machine (SVM) was 100% accurate when using either a linear or a radial basis Gaussian kernel, and the total image processing time was 32.75 s on a midrange PC station. It is concluded that the presented cerebral AVM detection system is both simple and accurate, and its processing time makes it compatible for use in a clinical environment, should it performance be confirmed with a larger image database.