Zero-shot learning (ZSL) offers tremendous potential for histopathology image analysis, enabling models to generalize to unseen classes without extensive labeled data. Recent vision-language model (VLM) advancements have expanded ZSL capabilities, allowing task performance without task-specific fine-tuning. However, applying VLMs to histopathology presents considerable challenges due to the complexity of histopathological imagery and the nuanced nature of diagnostic tasks. We propose Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE), a novel framework for zero-shot histopathology image classification. MR-PHE mimics pathologists' workflow through multiresolution patch extraction to capture key cellular and tissue features. It introduces a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings, effectively combining local and global contextual information. Additionally, we develop a comprehensive prompt generation and selection framework, enriching class descriptions with domain-specific synonyms and clinically relevant features to enhance semantic understanding. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings, emphasizing diagnostically important regions during classification. Experimental results demonstrate MR-PHE significantly improves zero-shot classification performance on histopathology datasets, often surpassing fully supervised models, showing its effectiveness and potential to advance computational pathology.

The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, thus unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the success of deep learning methods in numerous object detection tasks, here we report a deep learning network: ACTIVE based on Double Branch Feature Extraction Network (DBFEN) and Cross-conjugate Feature Pyramid Network (CCFPN). DBFEN extracts visual features from tiny objects with a double branch structure, and CCFPN fuses the features extracted by DBFEN to enhance the description of the position and high-level semantic information. Our work is the pioneer of introducing deep learning approaches to the detection of sperms and impurities. Experiments show that the highest AP50of the sperm and impurity detection is 91.13% and 59.64%, which lead its competitors by a substantial margin and establish the state-of-the-art results in this problem. Code is available for readers’ free evaluation at https://github.com/anheqiao-neu/ACTIVE.

This study proposes a Generative Adversarial Network (GAN)-based data augmentation method to tackle the class imbalance issue in histopathological image classification. The proposed GAN generates high-quality images for minority classes, improving data diversity and classification performance. By integrating Global Context Attention (GCA) and UpBlock-CompRes modules, the GAN generates images that closely resemble real data, achieving superior results in FID, IS, PSNR, and SSIM compared to FastGAN. Experimental results demonstrate that the proposed GAN outperforms both the baseline without data augmentation and FastGAN, with noticeable improvements across key metrics. These enhancements highlight the effectiveness of our GAN in generating high-quality images and achieving more balanced classification results.