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Claim this profileWorks346 from public data
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer3,315
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
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- Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge1,414
The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
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- VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images790
An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge.
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- FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space650
This paper points out and solves a novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains.
- 3D deeply supervised network for automated segmentation of volumetric medical images627
The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.
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- DCAN: Deep contour-aware networks for object instance segmentation from histology images518
A novel deep contour‐aware network (DCAN) under a unified multi‐task learning framework for more accurate detection and segmentation of objects of interest from histology images is proposed.
- Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation450
This work presents a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain and outperforms the state-of-the-art domain adaptation approaches by a large margin.
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- Federated learning enables big data for rare cancer boundary detection398
The largest FL study to-date is presented, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature.
- Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation388
This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift, and extensively validated the method with a challenging application of cross-modality medical image segmentation of cardiac structures.
- 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes375
A novel 3D deeply supervised network (3D DSN) is presented which takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference and introduces a deep supervision mechanism during the learning process to combat potential optimization difficulties.
- MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images372
A fully convolutional neural network is proposed that counters the loss of information caused by max‐pooling by re‐introducing the original image at multiple points within the network, and introduces random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity.
- Harmonizing Transferability and Discriminability for Adapting Object Detectors348
A Hierarchical Transferability Calibration Network (HTCN) that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability is proposed.
- Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma345
The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival.
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- Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss336
An unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations for cardiac structures segmentations is proposed, and very promising results are achieved.
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- Shape-Aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains295
A novel shape-aware meta-learning scheme is presented to improve the model generalization in prostate MRI segmentation, and outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
- Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study282
The federated learning algorithms are explored to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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