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Found 15 papers, 8 papers with code
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography
In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation.
Camera Adaptation for Fundus-Image-Based CVD Risk Estimation
Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras.
Medical Visual Question Answering: A Survey
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images
In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images.
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images.
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images
Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch.
Region and Object based Panoptic Image Synthesis through Conditional GANs
Our contribution consists of the proposal of a significant task worth investigating and a naive baseline of solving it.
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion
Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis.
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
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Nuclear Segmentation
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3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.
