Search Results for author:
Found 25 papers, 15 papers with code
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks.
Lesion Guided Explainable Few Weak-shot Medical Report Generation
To this end, we propose a lesion guided explainable few weak-shot medical report generation framework that learns correlation between seen and novel classes through visual and semantic feature alignment, aiming to generate medical reports for diseases not observed in training.
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture
These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.
Personalizing Federated Medical Image Segmentation via Local Calibration
Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site.
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information.
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.
Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports
Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning.
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification
Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantages of both unsupervised and (pseudo-) supervised learning on the base dataset.
Boundary-aware Transformers for Skin Lesion Segmentation
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.
Ranked #4 on
Lesion Segmentation
on ISIC 2018
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.
nnFormer: Interleaved Transformer for Volumetric Segmentation
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Superpixel-Guided Label Softening for Medical Image Segmentation
If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images.
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.
Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences
With the uniform distribution of random points, our proposed method achieves more accurate results compared with other methods, which demonstrates the robustness and accuracy for the volume calculation of CT lung lesions.
