no code implementations • 29 Jan 2024 • Yuxuan Sun, Hao Wu, Chenglu Zhu, Sunyi Zheng, Qizi Chen, Kai Zhang, Yunlong Zhang, Dan Wan, Xiaoxiao Lan, Mengyue Zheng, Jingxiong Li, Xinheng Lyu, Tao Lin, Lin Yang
To address this, we introduce PathMMU, the largest and highest-quality expert-validated pathology benchmark for Large Multimodal Models (LMMs).
no code implementations • 5 Jan 2024 • Sunyi Zheng, Xiaonan Cui, Yuxuan Sun, Jingxiong Li, Honglin Li, Yunlong Zhang, Pingyi Chen, Xueping Jing, Zhaoxiang Ye, Lin Yang
Additionally, we assess the robustness of PathCLIP in the task of image-image retrieval, revealing that PathCLIP performs less effectively than PLIP on Osteosarcoma but performs better on WSSS4LUAD under diverse corruptions.
no code implementations • 3 Jan 2024 • Shichuan Zhang, Sunyi Zheng, Zhongyi Shui, Honglin Li, Lin Yang
Using multi-modal data, whole slide images (WSIs) and clinical information, can improve the performance of deep learning models in the diagnosis of axillary lymph node metastasis.
1 code implementation • 27 Nov 2023 • Pingyi Chen, Honglin Li, Chenglu Zhu, Sunyi Zheng, Zhongyi Shui, Lin Yang
We benchmark our model on the largest subset of TCGA-PathoText.
1 code implementation • 27 Nov 2023 • Zhongyi Shui, Yunlong Zhang, Kai Yao, Chenglu Zhu, Sunyi Zheng, Jingxiong Li, Honglin Li, Yuxuan Sun, Ruizhe Guo, Lin Yang
In this paper, we present a novel prompt-driven framework that consists of a nucleus prompter and SAM for automatic nucleus instance segmentation.
no code implementations • 21 Nov 2023 • Honglin Li, Yunlong Zhang, Chenglu Zhu, Jiatong Cai, Sunyi Zheng, Lin Yang
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers.
1 code implementation • 14 Nov 2023 • Yunlong Zhang, Yuxuan Sun, Sunyi Zheng, Zhongyi Shui, Chenglu Zhu, Lin Yang
Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge.
1 code implementation • 13 Nov 2023 • Yunlong Zhang, Honglin Li, Yuxuan Sun, Sunyi Zheng, Chenglu Zhu, Lin Yang
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting.
1 code implementation • 22 Aug 2023 • Pingyi Chen, Chenglu Zhu, Zhongyi Shui, Jiatong Cai, Sunyi Zheng, Shichuan Zhang, Lin Yang
The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps.
no code implementations • 25 Jun 2023 • Jingxiong Li, Sunyi Zheng, Zhongyi Shui, Shichuan Zhang, Linyi Yang, Yuxuan Sun, Yunlong Zhang, Honglin Li, Yuanxin Ye, Peter M. A. van Ooijen, Kang Li, Lin Yang
This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results.
no code implementations • 14 Jun 2023 • Zhongyi Shui, Yizhi Zhao, Sunyi Zheng, Yunlong Zhang, Honglin Li, Shichuan Zhang, Xiaoxuan Yu, Chenglu Zhu, Lin Yang
Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training.
1 code implementation • 24 May 2023 • Yuxuan Sun, Chenglu Zhu, Sunyi Zheng, Kai Zhang, Lin Sun, Zhongyi Shui, Yunlong Zhang, Honglin Li, Lin Yang
Secondly, by leveraging the collected data, we construct PathCLIP, a pathology-dedicated CLIP, to enhance PathAsst's capabilities in interpreting pathology images.
1 code implementation • CVPR 2023 • Honglin Li, Chenglu Zhu, Yunlong Zhang, Yuxuan Sun, Zhongyi Shui, Wenwei Kuang, Sunyi Zheng, Lin Yang
Our framework is evaluated on five pathology WSI datasets on various WSI heads.
no code implementations • 5 Mar 2023 • Zhongyi Shui, Sunyi Zheng, Chenglu Zhu, Shichuan Zhang, Xiaoxuan Yu, Honglin Li, Jingxiong Li, Pingyi Chen, Lin Yang
Unlike mainstream PCD methods that rely on intermediate density map representations, the Point-to-Point network (P2PNet) has recently emerged as an end-to-end solution for PCD, demonstrating impressive cell detection accuracy and efficiency.
no code implementations • 14 Oct 2022 • Pingyi Chen, Chenglu Zhu, Zhongyi Shui, Jiatong Cai, Sunyi Zheng, Shichuan Zhang, Lin Yang
To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task.
no code implementations • 1 Jul 2022 • Zhongyi Shui, Shichuan Zhang, Chenglu Zhu, BingChuan Wang, Pingyi Chen, Sunyi Zheng, Lin Yang
Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification.
no code implementations • 1 Jul 2022 • Sunyi Zheng, Jingxiong Li, Zhongyi Shui, Chenglu Zhu, Yunlong Zhang, Pingyi Chen, Lin Yang
Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities.
1 code implementation • 30 Jun 2022 • Yunlong Zhang, Yuxuan Sun, Honglin Li, Sunyi Zheng, Chenglu Zhu, Lin Yang
Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation.
no code implementations • 13 Jan 2020 • Sunyi Zheng, Ludo J. Cornelissen, Xiaonan Cui, Xueping Jing, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen
Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94. 2% with 1. 0 false positive/scan and a sensitivity of 96. 0% with 2. 0 false positives/scan.
no code implementations • 11 Apr 2019 • Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen
Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans.