Tumor Segmentation

221 papers with code • 3 benchmarks • 9 datasets

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.

Libraries

Use these libraries to find Tumor Segmentation models and implementations

H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images

JinPLu/H2ASeg 27 Mar 2024

However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.

1
27 Mar 2024

Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

qdaiing/fedmema 18 Mar 2024

In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.

1
18 Mar 2024

Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation

yaziciz/GLIMS 15 Mar 2024

In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT).

1
15 Mar 2024

SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation

chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation 24 Jan 2024

Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models which are still at an all-time high, that performance and efficiency under clinical application scenarios are limited.

0
24 Jan 2024

CT Liver Segmentation via PVT-based Encoding and Refined Decoding

debeshjha/pvtformer 17 Jan 2024

Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.

7
17 Jan 2024

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

d3b-center/peds-brain-auto-seg-public 16 Jan 2024

External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0. 87+/-0. 13 (0. 91) and 0. 83+/-0. 18 (0. 89), respectively.

1
16 Jan 2024

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

datct00/Beyond-Traditional-Approaches-Multi-Task-Network-for-Breast-Ultrasound-Diagnosis 14 Jan 2024

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective.

2
14 Jan 2024

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

wetothemoon/braintumorsegmentation 31 Dec 2023

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%.

0
31 Dec 2023

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

boqian333/e2enet-medical 7 Dec 2023

E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.

9
07 Dec 2023