Papers with Code 아카이브
Medical Image Segmentation
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Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation
Text-guided multi-stage cross-perception network for medical image segmentation
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
PixCell: A generative foundation model for digital histopathology images
SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
Beyond Pixel Agreement: Large Language Models as Clinical Guardrails for Reliable Medical Image Segmentation
Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks
LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation
Lung Nodule Segmentation: Exploring Data Efficiency and Advanced Architectures
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation
TK-Mamba: Marrying KAN with Mamba for Text-Driven 3D Medical Image Segmentation
MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation
Unsupervised cell segmentation by fast Gaussian Processes
FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation
A Unified Multi-Scale Attention-Based Network for Automatic 3D Segmentation of Lung Parenchyma & Nodules In Thoracic CT Images
CENet: Context Enhancement Network for Medical Image Segmentation
How We Won the ISLES'24 Challenge by Preprocessing
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation
SAMba-UNet: Synergizing SAM2 and Mamba in UNet with Heterogeneous Aggregation for Cardiac MRI Segmentation
Auto-nnU-Net: Towards Automated Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.