1 code implementation • 12 Nov 2024 • Cheng Jin, Luyang Luo, Huangjing Lin, Jun Hou, Hao Chen
Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies.
no code implementations • 21 Sep 2024 • Hao Jiang, Runsheng Liu, Yanning Zhou, Huangjing Lin, Hao Chen
To this end, we propose a holistic and historical instance comparison approach for cervical cell detection.
1 code implementation • 21 Sep 2024 • Runsheng Liu, Hao Jiang, Yanning Zhou, Huangjing Lin, Liansheng Wang, Hao Chen
Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of different structures.
no code implementations • 22 Jul 2024 • Yingxue Xu, Yihui Wang, Fengtao Zhou, Jiabo Ma, Shu Yang, Huangjing Lin, Xin Wang, Jiguang Wang, Li Liang, Anjia Han, Ronald Cheong Kin Chan, Hao Chen
To our knowledge, this is the first attempt to incorporate multimodal knowledge at the slide level for enhancing pathology FMs, expanding the modelling context from unimodal to multimodal knowledge and from patch-level to slide-level.
1 code implementation • 23 Jun 2023 • Zhizhong Chai, Luyang Luo, Huangjing Lin, Pheng-Ann Heng, Hao Chen
To tackle this challenge, the literature on object detection has witnessed an increase of weakly-supervised and semi-supervised approaches, yet still lacks a unified framework that leverages various forms of fully-labeled, weakly-labeled, and unlabeled data.
no code implementations • 30 May 2023 • Yanwen Li, Luyang Luo, Huangjing Lin, Pheng-Ann Heng, Hao Chen
To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches.
no code implementations • 5 Jul 2022 • Zhizhong Chai, Huangjing Lin, Luyang Luo, Pheng-Ann Heng, Hao Chen
In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance.
no code implementations • 21 Apr 2021 • Luyang Luo, Hao Chen, Yongjie Xiao, Yanning Zhou, Xi Wang, Varut Vardhanabhuti, Mingxiang Wu, Chu Han, Zaiyi Liu, Xin Hao Benjamin Fang, Efstratios Tsougenis, Huangjing Lin, Pheng-Ann Heng
The models were also compared to radiologists on a subset of the internal testing set (n=496).
1 code implementation • 7 Apr 2021 • Luyang Luo, Hao Chen, Yanning Zhou, Huangjing Lin, Pheng-Ann Pheng
Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations.
no code implementations • 7 Apr 2021 • Zhizhong Chai, Luyang Luo, Huangjing Lin, Hao Chen, Anjia Han, Pheng-Ann Heng
Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels.
1 code implementation • 7 Apr 2021 • Yanwen Li, Luyang Luo, Huangjing Lin, Hao Chen, Pheng-Ann Heng
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide.
no code implementations • 13 Oct 2020 • Shujun Wang, Yaxi Zhu, Lequan Yu, Hao Chen, Huangjing Lin, Xiangbo Wan, Xinjuan Fan, Pheng-Ann Hen
The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis.
1 code implementation • 21 Jul 2020 • Yanning Zhou, Hao Chen, Huangjing Lin, Pheng-Ann Heng
The teacher's self-ensemble predictions from $K$-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student.
no code implementations • 3 May 2019 • Zixu Zhao, Huangjing Lin, Hao Chen, Pheng-Ann Heng
Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis.
no code implementations • 13 Aug 2017 • Qi Dou, Hao Chen, Yueming Jin, Huangjing Lin, Jing Qin, Pheng-Ann Heng
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment.
no code implementations • 30 Jul 2017 • Huangjing Lin, Hao Chen, Qi Dou, Liansheng Wang, Jing Qin, Pheng-Ann Heng
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.