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Found 12 papers, 4 papers with code
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
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.
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
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.
ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
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.
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
The models were also compared to radiologists on a subset of the internal testing set (n=496).
Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection
Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels.
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide.
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
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.
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis.
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
The teacher's self-ensemble predictions from $K$-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student.
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis
Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis.
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
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
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.
