The field of digital pathology is rapidly advancing, with a focus on developing innovative methods for spatial gene expression prediction, skin lesion segmentation, and whole slide image classification. Recent research has highlighted the importance of leveraging sparse local data for scalable and cost-effective spatial gene expression mapping, as well as the need for effective selection of training subsets to annotate from unlabelled data. Noteworthy papers include: ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction, which proposes a novel framework for predicting spatial transcriptomic landscapes from H&E-stained histology images. An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation, which introduces a bespoke cluster-based image selection process for improving model performance in minimal supervision settings. Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology, which utilizes pathological prior knowledge from language models to identify crucial local tissue types for WSI classification. Histomorphology-driven multi-instance learning for breast cancer WSI classification, which explicitly incorporates histomorphology information into WSI classification. Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology, which investigates the potential of unsupervised automatic data curation at the tile-level. A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images, which proposes a prototype-guided approach for refining coarse annotations. Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology, which introduces a cross-modal unsupervised slide representation learning framework. Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning, which develops predictive models for HER2 3-way classification to enhance prognosis.