Papers with Code 아카이브

Lesion Segmentation

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평가표: 9
보존된 평가 기록: 42

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.

<span style="color:grey; opacity: 0.6">( Image credit: D-UNet )</span>

코드

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

https://github.com/mjahanifar/mDRFI_matlab

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Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images

https://github.com/ml-ailab/ophglm

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OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

https://github.com/mrkolarik/transfer2d3d

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Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

https://github.com/nabsabraham/focal-tversky-unet

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A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/naga-karthik/continual-learning-ms

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Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

https://github.com/nanase1025/m-sam

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Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation

https://github.com/nataliiamolch/interpret-lesion-unc

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Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

https://github.com/nataliiamolch/ndsc

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Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation

https://github.com/neerakara/tta-1d-density-matching

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A Field of Experts Prior for Adapting Neural Networks at Test Time

https://github.com/nekopii/tjdr

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TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset

https://github.com/neuropoly/ivado-medical-imaging

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Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

https://github.com/nitr098/attswinunet

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

https://github.com/nkicsl/Fundus_Review

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Applications of Deep Learning in Fundus Images: A Review

https://github.com/nkuhealong/pmcnet

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Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

https://github.com/nqnguyen812/mambau-lite

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MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

https://github.com/nyukat/GMIC

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An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

https://github.com/osupcvlab/mobileunetr

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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

https://github.com/pashtari/factorizer

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Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

https://github.com/pbizopoulos/comprehensive-comparison-of-deep-learning-models-for-lung-and-covid-19-lesion-segmentation-in-ct

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Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans

https://github.com/philip-mueller/equivariant-deep-dmri

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Rotation-Equivariant Deep Learning for Diffusion MRI

https://github.com/poiuohke/UDC-Net

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Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

https://github.com/pooya-mohammadi/unet-skin-cancer

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U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation

https://github.com/prantik-pdeb/best-les

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BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision

https://github.com/precision-vision/oed-classification-segmentation

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An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images

https://github.com/pykao/ISLES2017-mRS-prediction

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Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

https://github.com/raun1/ASC-NET

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ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation

https://github.com/raun1/LITS_Hybrid_Comp_Net

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Hybrid Cascaded Neural Network for Liver Lesion Segmentation

https://github.com/retinal-research/redot

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OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

https://github.com/rezazad68/BCDU-Net

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Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

https://github.com/rightl/autopet

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AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT

https://github.com/rongtao-xu/skinformer

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SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

https://github.com/rongzhao-zhang/ag-crc

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AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge

https://github.com/ruizhe-l/semi-segmentation

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A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

https://github.com/rvignav/CT2Xray

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COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs

https://github.com/saniaesk/skin-lesion-segmentation

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Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling

https://github.com/scan-nrad/e3nn_unet

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Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation

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