matlok
's Collections
Papers - Image - Segmentation
updated
Image Segmentation using U-Net Architecture for Powder X-ray Diffraction
Images
Paper
•
2310.16186
•
Published
•
2
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor
Segmentation from CT Volumes
Paper
•
1709.07330
•
Published
•
2
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic
Tumors on CT scans
Paper
•
1801.08599
•
Published
•
2
RTSeg: Real-time Semantic Segmentation Comparative Study
Paper
•
1803.02758
•
Published
•
2
Generalizability vs. Robustness: Adversarial Examples for Medical
Imaging
Paper
•
1804.00504
•
Published
•
2
Hierarchical multi-class segmentation of glioma images using networks
with multi-level activation function
Paper
•
1810.09488
•
Published
•
2
IVD-Net: Intervertebral disc localization and segmentation in MRI with a
multi-modal UNet
Paper
•
1811.08305
•
Published
•
2
A multi-path 2.5 dimensional convolutional neural network system for
segmenting stroke lesions in brain MRI images
Paper
•
1905.10835
•
Published
•
2
Enforcing temporal consistency in Deep Learning segmentation of brain MR
images
Paper
•
1906.07160
•
Published
•
3
SkipNet: Learning Dynamic Routing in Convolutional Networks
Paper
•
1711.09485
•
Published
•
2
Bias Loss for Mobile Neural Networks
Paper
•
2107.11170
•
Published
•
2
Skip-Connected Neural Networks with Layout Graphs for Floor Plan
Auto-Generation
Paper
•
2309.13881
•
Published
•
2
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on
3D Diffusion Model
Paper
•
2401.17593
•
Published
•
2
Inter-Scale Dependency Modeling for Skin Lesion Segmentation with
Transformer-based Networks
Paper
•
2310.13727
•
Published
•
2
3D Medical Image Segmentation based on multi-scale MPU-Net
Paper
•
2307.05799
•
Published
•
2
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin
Lesion Segmentation
Paper
•
2210.16898
•
Published
•
2
Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
Paper
•
2110.08776
•
Published
•
2
Enforcing Morphological Information in Fully Convolutional Networks to
Improve Cell Instance Segmentation in Fluorescence Microscopy Images
Paper
•
2106.05843
•
Published
•
2
Saliency-Guided Deep Learning Network for Automatic Tumor Bed Volume
Delineation in Post-operative Breast Irradiation
Paper
•
2105.02771
•
Published
•
2
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network
for Brain Tumor Segmentation
Paper
•
2009.06767
•
Published
•
2
The Effects of Image Pre- and Post-Processing, Wavelet Decomposition,
and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
Paper
•
1805.05239
•
Published
•
2
A joint 3D UNet-Graph Neural Network-based method for Airway
Segmentation from chest CTs
Paper
•
1908.08588
•
Published
•
2
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN
with Transformer Layers
Paper
•
2201.10981
•
Published
•
2
Meta-information-aware Dual-path Transformer for Differential Diagnosis
of Multi-type Pancreatic Lesions in Multi-phase CT
Paper
•
2303.00942
•
Published
•
2
Cross-Shaped Windows Transformer with Self-supervised Pretraining for
Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
Paper
•
2305.00385
•
Published
•
2
MAFormer: A Transformer Network with Multi-scale Attention Fusion for
Visual Recognition
Paper
•
2209.01620
•
Published
•
2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Paper
•
2103.14030
•
Published
•
4
A Novel Transformer Based Semantic Segmentation Scheme for
Fine-Resolution Remote Sensing Images
Paper
•
2104.12137
•
Published
•
2
Self-Supervised Learning with Swin Transformers
Paper
•
2105.04553
•
Published
•
2
Bootstrap your own latent: A new approach to self-supervised Learning
Paper
•
2006.07733
•
Published
•
2
Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC
Challenge
Paper
•
2202.13588
•
Published
•
2
From Modern CNNs to Vision Transformers: Assessing the Performance,
Robustness, and Classification Strategies of Deep Learning Models in
Histopathology
Paper
•
2204.05044
•
Published
•
2
Self-Supervised Vision Transformers Learn Visual Concepts in
Histopathology
Paper
•
2203.00585
•
Published
•
2
GasHis-Transformer: A Multi-scale Visual Transformer Approach for
Gastric Histopathological Image Detection
Paper
•
2104.14528
•
Published
•
2
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for
White Blood Cells
Paper
•
2401.07278
•
Published
•
2
Unifying Vision, Text, and Layout for Universal Document Processing
Paper
•
2212.02623
•
Published
•
10
Paper
•
2304.02643
•
Published
•
3
Noise-Aware Training of Layout-Aware Language Models
Paper
•
2404.00488
•
Published
•
7
Transferable and Principled Efficiency for Open-Vocabulary Segmentation
Paper
•
2404.07448
•
Published
•
11
RegionGPT: Towards Region Understanding Vision Language Model
Paper
•
2403.02330
•
Published
•
2
COCONut: Modernizing COCO Segmentation
Paper
•
2404.08639
•
Published
•
27
Efficient Transformer Encoders for Mask2Former-style models
Paper
•
2404.15244
•
Published
•
1
Mask2Former for Video Instance Segmentation
Paper
•
2112.10764
•
Published
•
1
Deep Residual Learning for Image Recognition
Paper
•
1512.03385
•
Published
•
6
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster
Pre-training on Web-scale Image-Text Data
Paper
•
2404.15653
•
Published
•
26
Interactive3D: Create What You Want by Interactive 3D Generation
Paper
•
2404.16510
•
Published
•
18
Florence-2: Advancing a Unified Representation for a Variety of Vision
Tasks
Paper
•
2311.06242
•
Published
•
84
SAM 2: Segment Anything in Images and Videos
Paper
•
2408.00714
•
Published
•
107
Surgical SAM 2: Real-time Segment Anything in Surgical Video by
Efficient Frame Pruning
Paper
•
2408.07931
•
Published
•
18