182 citations · 251 across the 18 of their papers we have counts for
18 papers
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
Hao Jiang, Cheng Jin, Huangjing Lin +15
Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems strug…
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
Cheng Jin, Luyang Luo, Huangjing Lin +2
Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of th…
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation
Runsheng Liu, Hao Jiang, Yanning Zhou +3
Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of di…
Holistic and Historical Instance Comparison for Cervical Cell Detection
Hao Jiang, Runsheng Liu, Yanning Zhou +2
Cytology screening from Papanicolaou (Pap) smears is a common and effective tool for the preventive clinical management of cervical cancer, where abnormal cell detection from whole…
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Yingxue Xu, Yihui Wang, Fengtao Zhou +16
Remarkable strides in computational pathology have been made in the task-agnostic foundation model that advances the performance of a wide array of downstream clinical tasks. Despi…
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
Zhizhong Chai, Luyang Luo, Huangjing Lin +2
Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based…
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
Yanwen Li, Luyang Luo, Huangjing Lin +2
Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based al…
ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
Zhizhong Chai, Huangjing Lin, Luyang Luo +2
Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotati…
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
Luyang Luo, Hao Chen, Yongjie Xiao +10
Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet an…
Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection
Zhizhong Chai, Luyang Luo, Huangjing Lin +3
Deep learning has achieved unprecedented success in various object detection tasks with huge amounts of labeled data. However, obtaining large-scale annotations for medical images…
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
Yanwen Li, Luyang Luo, Huangjing Lin +2
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide. Automatically segmenting lesions from chest Computed Tomo…
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
Luyang Luo, Hao Chen, Yanning Zhou +2
Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating r…
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
Shujun Wang, Yaxi Zhu, Lequan Yu +5
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how t…
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
Yanning Zhou, Hao Chen, Huangjing Lin +1
Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pi…
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
Luyang Luo, Hao Chen, Xi Wang +5
Accurate and automatic analysis of breast MRI plays an important role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, accur…
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis
Zixu Zhao, Huangjing Lin, Hao Chen +1
Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis. Recent convolutional neural network based app…
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
Qi Dou, Hao Chen, Yueming Jin +3
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 ye…
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Huangjing Lin, Hao Chen, Qi Dou +3
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years,…