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Tumor Segmentation

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.

Papers

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Showing 291–300 of 786 papers

TitleStatusHype
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationCode2
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images—0
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging—0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI—0
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