Search Results for author:
Found 21 papers, 11 papers with code
Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge.
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images
Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images.
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.
DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis
To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge.
Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Foremost, our work demonstrates that the VLPM pre-trained on natural image-text pairs exhibits astonishing potential for downstream tasks in the medical field as well.
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation
Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning.
3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis
(2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance.
Transformer based multiple instance learning for weakly supervised histopathology image segmentation
Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology.
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.
Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.
Atlas-Based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks
The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks.
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis.
Unsupervised End-to-end Learning for Deformable Medical Image Registration
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.
Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device
The feature learning framework is designed to extract low- and mid-level features.
Automatic Sleep Stage Classification
General Classification
+1
Learning Multi-level Features For Sensor-based Human Action Recognition
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
