most citedCGMI: Configurable General Multi-Agent Interaction Framework

6 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV20241 cited

Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections

Xitong Ling, Yuanyuan Lei, Jiawen Li +5

Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images…

cs.CV2024

Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image

Jiawen Li, Qiehe Sun, Renao Yan +7

With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer d…

cs.CV2024

RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification

Hongbo Chu, Qiehe Sun, Jiawen Li +5

Histopathological whole slide image (WSI) analysis with deep learning has become a research focus in computational pathology. The current paradigm is mainly based on multiple insta…

cs.AI20236 cited

CGMI: Configurable General Multi-Agent Interaction Framework

Shi Jinxin, Zhao Jiabao, Wang Yilei +3

Benefiting from the powerful capabilities of large language models (LLMs), agents based on LLMs have shown the potential to address domain-specific tasks and emulate human behavior…

q-bio.QM2023

The Whole Pathological Slide Classification via Weakly Supervised Learning

Qiehe Sun, Jiawen Li, Jin Xu +3

Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole sli…