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/cchen-cc/ma-sam

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MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

https://github.com/cgpxy/esknet

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ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

https://github.com/cgpxy/nu-net

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Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation

https://github.com/cgpzy/nu-net

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Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation

https://github.com/chenhong-zhou/OM-Net

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One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation

https://github.com/chenzhao2023/upmad_net_brainseg

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UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

https://github.com/chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation

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SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation

https://github.com/clinical-and-translational-imaging-lab/brats-e1d3

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E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge

https://github.com/cocofeat/tbrats

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TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cucl-2/dc-seg

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DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cv516buaa/mmotu_ds2net

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MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

https://github.com/cvlab-stonybrook/SelfMedMAE

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Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

https://github.com/cxh-research/smaformer

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SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation

https://github.com/d3b-center/peds-brain-auto-seg-public

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Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

https://github.com/datct00/Beyond-Traditional-Approaches-Multi-Task-Network-for-Breast-Ultrasound-Diagnosis

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Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

https://github.com/dbouget/Raidionics

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Preoperative brain tumor imaging: models and software for segmentation and standardized reporting

https://github.com/dbouget/Raidionics-Slicer

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Preoperative brain tumor imaging: models and software for segmentation and standardized reporting

https://github.com/dbouget/validation_metrics_computation

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Preoperative brain tumor imaging: models and software for segmentation and standardized reporting

https://github.com/ddrrnn123/omni-seg

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Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data

https://github.com/debeshjha/pvtformer

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CT Liver Segmentation via PVT-based Encoding and Refined Decoding

https://github.com/deeppathology/multiscanner_scc

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Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

https://github.com/delugewalker/CGA-U-Net

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Category Guided Attention Network for Brain Tumor Segmentation in MRI

https://github.com/dskhanirfan/fets2022

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Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation

https://github.com/eceisik/FeTS_Challenge_METU_FL_Team

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Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation

https://github.com/endoluminalsurgicalvision-imr/acnorm

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AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization

https://github.com/fenghetan9/cmu-net

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CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

https://github.com/fiy2w/treatment-aware-longitudinal-registration

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To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection

https://github.com/fjr9516/da_nnunet

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Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation

https://github.com/florentinbieder/PatchDDM-3D

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Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing

https://github.com/frankkramer-lab/MIScnn

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MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

https://github.com/gama-ufsc/brain-extraction-for-tumor-segmentation

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Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?

https://github.com/glejdis/whole-slide-mil-for-predicting-axillary-lymph-node-metastasis

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Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

https://github.com/guigzos/crossmodalgba

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Cross-modal tumor segmentation using generative blending augmentation and self training

https://github.com/haail/fins-for-biomedical-imaging

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Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks

https://github.com/hamghalam/MGP-VAE

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Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation

https://github.com/haranrk/DigiPathAI

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A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

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