Search Results for author: Sunyi Zheng

Found 20 papers, 8 papers with code

PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology

no code implementations29 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).

Benchmarking PathCLIP for Pathology Image Analysis

no code implementations5 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.

Benchmarking Decision Making +4

Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis

no code implementations3 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.

Decision Making whole slide images

Unleashing the Power of Prompt-driven Nucleus Instance Segmentation

1 code implementation27 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.

Image Segmentation Instance Segmentation +3

Test-Time Training for Semantic Segmentation with Output Contrastive Loss

1 code implementation14 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.

Domain Adaptation Image Classification +1

Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification

1 code implementation13 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.

Image Classification Multiple Instance Learning

Masked conditional variational autoencoders for chromosome straightening

no code implementations25 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.

Semi-supervised Cell Recognition under Point Supervision

no code implementations14 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.

whole slide images

PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology

1 code implementation24 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.

Instruction Following Language Modelling +1

DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection

no code implementations5 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.

Cell Detection

Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images

no code implementations14 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.

Image Segmentation Medical Image Segmentation +3

ChrSNet: Chromosome Straightening using Self-attention Guided Networks

no code implementations1 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.

End-to-end cell recognition by point annotation

no code implementations1 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.

Cell Detection Multi-Task Learning

Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

1 code implementation30 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.


Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification

no code implementations13 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.

Lung Nodule Detection

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

no code implementations11 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.

Computed Tomography (CT) Lung Nodule Detection

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