Papers with Code 아카이브

Tumor Segmentation

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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/sergiormpereira/rr_segse

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Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI

https://github.com/shijun18/h-denseformer

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H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020

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A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

https://github.com/sky-visionX/TexLiverNet

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TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation

https://github.com/sotiraslab/QCResUNet

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QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction

https://github.com/sotiraslab/dlk

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D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

https://github.com/soumbane/dualselfdistillation

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Volumetric medical image segmentation through dual self-distillation in U-shaped networks

https://github.com/sveinnpalsson/semivaebrats

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Semi-Supervised Variational Autoencoder for Survival Prediction

https://github.com/tianzetang0504/ukan-ep

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UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation

https://github.com/timothy102/Brain-FMRI

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AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

https://github.com/tuat-novice/maprotonet

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MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification

https://github.com/ustguy/flare23

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Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation

https://github.com/vangorade/rethinkingkd_isbi24

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Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation

https://github.com/vemundfredriksen/lungtumormask

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Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation

https://github.com/vinyehshaw/hff

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective

https://github.com/vivek231/breast_tumor_segmentation

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Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network

https://github.com/voreille/hecktor

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

https://github.com/vpulab/med-sam-brain

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GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI Data

https://github.com/wangkaiwan/hecktor-2022-airt

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Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

https://github.com/wangli100209/PDPNet

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Progressive Dual Priori Network for Generalized Breast Tumor Segmentation

https://github.com/wangshansong1/Triad

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Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging

https://github.com/wendywaaaaang/a4-unet

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A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation

https://github.com/wetothemoon/braintumorsegmentation

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Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction

https://github.com/whq-xxh/Active-GTV-Seg

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Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

https://github.com/willbeanyang/swin-der

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Optimizing Medical Image Segmentation with Advanced Decoder Design

https://github.com/windstormer/Cfd-CAM

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A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

https://github.com/windstormer/ame-cam

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AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

https://github.com/wltyby/hnts-mrg2024_train_code

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Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet

https://github.com/woodywff/brats_2019

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3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

https://github.com/woodywff/nas_3d_unet

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Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging

https://github.com/wujunde/medsegdiff

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MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/xrt11/tumor-segmentation

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A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation

https://github.com/xyx1024/SimTxtSeg

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SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

https://github.com/yankai96/diffugts

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models

https://github.com/yankai96/zept

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ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

https://github.com/yaozhang93/mmformer

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mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

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