Papers with Code 아카이브

Tumor Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 4
보존된 평가 기록: 5

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Raykoooo/CAC-UNet

공식 코드

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

https://github.com/RichardObi/SimulatingDCE

공식 코드

Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks

https://github.com/ShawkhIbneRashid/medical-images-sr

공식 코드

Single MR Image Super-Resolution using Generative Adversarial Network

https://github.com/Stefan-Yu404/MP-UNet

공식 코드

3D Medical Image Segmentation based on multi-scale MPU-Net

https://github.com/TIO-IKIM/PhaseGen

공식 코드

PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation

https://github.com/Wangyixinxin/ACN

공식 코드

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

https://github.com/Wenxuan-1119/TransBTS

공식 코드

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/YaoZhang93/MAML

공식 코드

Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation

https://github.com/YaoZhang93/TumorCP

공식 코드

TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation

https://github.com/YingdaXia/SynthCP

공식 코드

Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

https://github.com/ZhihuaLiuEd/SoTA-Brain-Tumor-Segmentation

공식 코드

Deep Learning Based Brain Tumor Segmentation: A Survey

https://github.com/ZhihuaLiuEd/canetbrats

공식 코드

CANet: Context Aware Network for 3D Brain Glioma Segmentation

https://github.com/aarhus-radonc-ai/gradientseghnts

공식 코드

Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy

https://github.com/aecelaya/gen-surf-loss

공식 코드

A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation

https://github.com/aecelaya/mg-nets

공식 코드

FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation

https://github.com/ahukui/SIPNet

공식 코드

Selective Information Passing for MR/CT Image Segmentation

https://github.com/alibaba-damo-academy/pixel-lesion-patient-network

공식 코드

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

https://github.com/anok3111/loco

공식 코드

Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation

https://github.com/applicationtechnologyofmedicalbigdata/pfednet-code

공식 코드

Medical Federated Model with Mixture of Personalized and Sharing Components

https://github.com/arcadelab/counterfactual_diffusion

공식 코드

Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs

https://github.com/arnold-jun/fed-munet

공식 코드

Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation

https://github.com/ashnilkumar/colearn

공식 코드

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer

https://github.com/askerlee/segtran

공식 코드

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/auroua/hrstnet

공식 코드

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

https://github.com/avakanski/Attention-Enriched-DL-Model-for-Breast-Tumor-Segmentation

공식 코드

Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

https://github.com/bbqtime/sine-wave-normalization-for-deep-learning-based-tumor-segmentation-in-ct-pet

공식 코드

Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging

https://github.com/bidur-khanal/mvaal-medical-images

공식 코드

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

https://github.com/billhhh/MCKD

공식 코드

Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data

https://github.com/biomedia-mbzuai/brats2024_biomediambz

공식 코드

On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks

https://github.com/boqian333/e2enet-medical

공식 코드

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

https://github.com/brainlesion/brats

공식 코드

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

https://github.com/brats-inpainting/2023_challenge

공식 코드

The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

https://github.com/bupt-ai-cz/CAC-UNet-DigestPath2019

공식 코드

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

https://github.com/camera-mri/spark2024

공식 코드

Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset

https://github.com/ccarliu/m3ae

공식 코드

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cchen-cc/Robust-Mseg

공식 코드

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.