Search Results for author: Huangjing Lin

Found 16 papers, 6 papers with code

HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification

1 code implementation12 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.

Classification Contrastive Learning +3

Holistic and Historical Instance Comparison for Cervical Cell Detection

no code implementations21 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.

Cell Detection whole slide images

GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation

1 code implementation21 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.

Instance Segmentation Segmentation +1

A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

no code implementations22 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.

whole slide images

Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images

1 code implementation23 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.

Fracture detection object-detection

Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images

no code implementations30 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.

Image Segmentation Image Super-Resolution +4

ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans

no code implementations5 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.

Fracture detection Object +1

OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays

1 code implementation7 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.

Lesion Detection

Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

no code implementations7 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.

Cell Detection Metric Learning +2

Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

1 code implementation21 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.

Instance Segmentation Knowledge Distillation +2

PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

no code implementations3 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.

Computational Efficiency Decoder +2

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

no code implementations13 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.

ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images

no code implementations30 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.

Breast Cancer Detection whole slide images

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