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PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

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Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans

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Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images

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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

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The Liver Tumor Segmentation Benchmark (LiTS)

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inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-snapshot

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

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Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

inferredcites →https://doi.org/10.1155/2018/3815346openalex-snapshot

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

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SIRT7 Regulates the Vascular Smooth Muscle Cells Proliferation and Migration via Wnt/β-Catenin Signaling Pathway

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

inferred← citeshttps://doi.org/10.1007/978-3-031-11154-9_4openalex-snapshot

Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images

inferred← citeshttps://doi.org/10.1007/978-3-031-16452-1_21openalex-snapshot

An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions

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Automatic Segmentation of Liver Tumor from Multi-phase Contrast-Enhanced CT Images Using Cross-Phase Fusion Transformer

inferred← citeshttps://doi.org/10.1007/978-3-031-65640-8_11openalex-snapshot

Enhanced Liver Lesion Detection in Multi-Phase CT Images Using Unsupervised Domain Adaptation and Feature Generalization

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LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning

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Domain Adaptation and Generalization for Medical Image Analysis

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A Vision-Language Model for Focal Liver Lesion Classification

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Improved Mask R-CNN with Deformable Convolutions for Accurate Liver Lesion Detection in Multiphase CT Images

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Unsupervised Domain Adaptation with Adversarial Learning for Liver Tumors Detection in Multi-phase CT Images

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Deep Liver Lesion AI System: A Liver Lesion Diagnostic System Using Deep Learning in Multiphase CT

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Improved Liver Lesion Detection Using Foundation Model: A Novel Approach

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Deep Neural Network-Based Classification of Focal Liver Lesions Using Phase-Shuffle Prediction Pre-training

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An Efficient Dual-Pooling Channel Attention Module for Weakly-Supervised Liver Lesion Classification on CT Images

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Advances in liver, liver lesion, hepatic vasculature, and biliary segmentation: a comprehensive review of traditional and deep learning approaches

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Unified automated deep learning framework for segmentation and classification of liver tumors

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Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net

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DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images

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Reciprocal cross-modal guidance for liver lesion segmentation from multiple phases under incomplete overlap

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MBUSNet: Multimodal breast ultrasound segmentation with attention-guided rectification and boundary enhancement

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PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation

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Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans

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Multi-phase feature-aligned fusion model for automated colorectal cancer segmentation in contrast-enhanced CT scans

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PARF-Net: Phase-adaptive robust fusion for liver tumor segmentation with missing-phase CECT

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Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

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