25 skills found
jponttuset / McgMultiscale Combinatorial Grouping - Object Proposals and Segmentation
lim-anggun / FgSegNetFgSegNet: Foreground Segmentation Network, Foreground Segmentation Using Convolutional Neural Networks for Multiscale Feature Encoding
mrFahrenhiet / CrackSegmentationDeepLearningMultiscale Attention Based Efficient U-Net for Crack Segmentation, segments a RGB image into 2 classes crack and non-crack, this method obtained SOTA results on Crack500 dataset
ChenLiu-1996 / CUTS[MICCAI 2024] CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
DrWuHonglin / CMTFNetThis is the code of CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote Sensing Image Semantic Segmentation
rkyuca / MedvtPyTorch implementation of MED-VT: Multiscale Encoder-Decoder Video Transformer with Application to Object Segmentation
wjiazheng / SwinPA NetSwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation
konopczynski / Vessel3DDLAutomated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images
Greak-1124 / LMFFNetReal-time semantic segmentation is widely used in the field of autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation tasks. In this paper, we introduce a Lightweight-Multiscale-Feature-Fusion Network (LMFFNet) mainly composed of three types of components: Split-Extract-Merge Bottleneck (SEM-B) block, Features Fusion Module (FFM), and Multiscale Attention Decoder (MAD). The SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy. The MAD well recovers the details of the input images through the attention mechanism. Two networks combined with different components are proposed based on the LMFFNet model. Without pretraining, the smaller network of LMFFNet-S achieves 72.7% mIoU on Cityscapes test set at the 512×1024 resolution with only 1.1 M parameters at a reference speed of 98.9 fps running on a GTX1080Ti GPU while the larger version of LMFFNet-L achieves 74.7% mIoU with 1.4 M parameters at 89.6 fps. Besides, 67.7% mIoU at 208.9 fps and 70.3% mIoU at 72.4 fps are respectively achieved for 360 × 480 and 720 × 960 resolutions on CamVid test set using LMFFNet-S while LMFFNet--L achieves 68.1% mIoU at 182.9 fps and 71.0% mIoU at 66.5 fps, correspondingly. The proposed LMFFNets make an adequate trade-off between accuracy and parameter size for real-time inference for semantic segmentation tasks.
BEBC-MIA / Retinal Lesion SegmentationDiabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions helps early diagnosis of DR. However, due to the scarcity of pixel-level annotations and the large diversity between different types of DR lesions, the existing deep learning methods are very challenging in performing segmentation on retinal images. In this study, we propose a novel data augmentation method based on Poisson-blending (PB) algorithm to generate synthetic images, which can be easily adapted to other medical anomaly segmentation tasks to alleviate the training data scarcity issue. We also proposed a CNN architecture for the simultaneous segmentation of multiscale anomaly signs. The performances are compared with the state-of-the-art methods on Indian Diabetic Retinopathy Image Dataset (IDRiD) and e-ophtha datasets, both widely used in the research community. The results indicate that the proposed method significantly outperforms the state-of-the-art methods.
StevenAZy / PyConvU NetA lightweight and multiscale network for biomedical image segmentation
LiBingyu01 / U3M[Pattern Recognition 2025 🌟]Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
lseventeen / MAA Net Vessel Segmentation[ICASSP2022]Multiscale attention aggregation network for 2d vessel segmentation
ubbp / MCD NetA Multiscale Vision-Text Collaborative Dual-Encoder for Referring RS Image Segmentation
1999luan / MSDANetMSDANet: A Multiscale Dilation Attention Network for Medical Image Segmentation
1027865 / MLRUPPMultiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation
linkenfaqiu / MMRAN这是《Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification》这篇文章的源代码。若要使用，请对data文件夹进行数据划分后，输入$python main.py train$
mangoyuan / MUIS(MICCAI2022) Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images
liyemei / AMFLAdversarial Multiscale Features Learning for Overlapping Chromosome Segmentation
jinggqu / SwitchOfficial PyTorch implementation of the study "Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation"
