Large language models (LLMs) like LLaMA, Baichuan and Bloom models show remarkable ability with instruction fine-tuning in many natural language tasks. Nevertheless, for the dialogue summarization task, which aims to generate summaries for different roles in dialogue, most of the state-of-the-art methods foucus on small models (e.g BART and BERT). Existing methods try to add task specified optimization on small models like adding global-local centrality score to models. In this paper, we propose an instruction fine-tuning model: Baichuan2-Sum, for role-oriented dialogue summarization. By setting different instructions for different roles, the model can learn from the dialogue interactions and output the desired summaries. Furthermore, we applied NEFTune technique to add suitable noise during training, improving the results. The experiments demonstrate that the proposed model achieves the new state-of-the-art results on two public dialogue summarization datasets: CSDS and SAMSUM. The Baichuan2-Sum model shows an improvement in Rouge scores on both datasets compared to the previously best-performing model. Notably, for the SAMSUM dataset, there is a 21% increase in the ROUGE-1 score, a 32% increase in the ROUGE-2 score, and a 9% increase in the ROUGE-L score. We have released our model and related codes to facilitate future studies in the dialogue summarization task.

MOTIVATION: Morphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations. RESULTS: To address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder-decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract structural information, while we create a morphology loss to produce the segmentation map. In addition, we develop a novel image augmentation algorithm (AugCut) to produce multiple views for self-supervised learning and enhance the network training performance. To validate the efficacy of our method, we applied our developed S4 method for RPE cell segmentation to a large set of flatmount fluorescent microscopy images, we compare our developed method for RPE cell segmentation with other state-of-the-art deep learning approaches. Compared with other state-of-the-art deep learning approaches, our method demonstrates better performance in both qualitative and quantitative evaluations, suggesting its promising potential to support large-scale cell morphological analyses in RPE aging investigations. AVAILABILITY AND IMPLEMENTATION: The codes and the documentation are available at: https://github.com/jkonglab/S4_RPE.