Liver segmentation network based on detail enhancement and multi-scale feature fusion

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 3, 2025

Due to the low contrast of abdominal CT (Computer Tomography) images and similar color shape liver other organs such as spleen, stomach, kidneys, segmentation presents significant challenges. Additionally, 2D obtained from different angles (such sagittal, coronal, transverse planes) increase diversity morphology complexity segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) improve feature learning thereby enhance performance. Furthermore, enable model better learn features at scales, Multi-Scale Feature Fusion module (MSFF) is added skip connections in model. The MSFF enhances capture global features, thus improving accuracy Through aforementioned research, network based on detail enhancement multi-scale fusion (DEMF-Net). We conducted extensive experiments LiTS17 dataset, results demonstrate that DEMF-Net achieved improvements across various evaluation metrics. Therefore, proposed can achieve precise

Язык: Английский

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Encoder-Free Multiaxis Physics-Aware Fusion Network for Remote Sensing Image Dehazing

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2023, Номер 61, С. 1 - 15

Опубликована: Янв. 1, 2023

Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose EMPF-Net, a novel encoder-free multi-axis physics-aware fusion network that exhibits both light-weighted characteristics efficiency. In our pipeline, contend conventional u-shaped networks allocate substantial resources to encode haze-degraded features, which play subordinate role in the reconstruction process. Consequently, encoder stages solely incorporate down-sampling operations. improve representation efficiency enhance generalization capabilities, devise partial queried learning block (MPQLB) primarily concentrates on dimension-wise queries, instead of relying strictly-correlated content input features. Furthermore, augment procedure by incorporating ground truth supervision into each stage via supervised cross-scale transposed attention module (SCTAM). It calculates maps under guidance clean images, suppressing less informative features propagate subsequent level. addition, address challenge ineffective intral-level feature fusion, result insufficient elimination information negatively impact quality reconstructed introduce intra-level (PIFM). This harnesses physical inversion model facilitate interaction alleviate interference dehazing-irrelevant information. Our proposed EMPF-Net is evaluated 12 publicly available datasets, experimental results substantiate superiority terms metrical scores visual quality, despite being equipped with modest parameter count 300 K. approach readily accessible at https://github.com/chdwyb/EMPF-Net.

Язык: Английский

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Rolling-Unet: Revitalizing MLP’s Ability to Efficiently Extract Long-Distance Dependencies for Medical Image Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence, Год журнала: 2024, Номер 38(4), С. 3819 - 3827

Опубликована: Март 24, 2024

Medical image segmentation methods based on deep learning network are mainly divided into CNN and Transformer. However, struggles to capture long-distance dependencies, while Transformer suffers from high computational complexity poor local feature learning. To efficiently extract fuse features long-range this paper proposes Rolling-Unet, which is a model combined with MLP. Specifically, we propose the core R-MLP module, responsible for dependency in single direction of whole image. By controlling combining modules different directions, OR-MLP DOR-MLP formed dependencies multiple directions. Further, Lo2 block proposed encode both context information without excessive burden. has same parameter size as 3×3 convolution. The experimental results four public datasets show that Rolling-Unet achieves superior performance compared state-of-the-art methods.

Язык: Английский

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PAMSNet: A medical image segmentation network based on spatial pyramid and attention mechanism

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106285 - 106285

Опубликована: Апрель 1, 2024

Язык: Английский

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UCM-Net: A lightweight and efficient solution for skin lesion segmentation using MLP and CNN

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106573 - 106573

Опубликована: Июль 3, 2024

Язык: Английский

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DSEUNet: A lightweight UNet for dynamic space grouping enhancement for skin lesion segmentation

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124544 - 124544

Опубликована: Дек. 1, 2024

Язык: Английский

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Skin cancer identification utilizing deep learning: A survey

IET Image Processing, Год журнала: 2024, Номер unknown

Опубликована: Сен. 2, 2024

Abstract Melanoma, a highly prevalent and lethal form of skin cancer, has significant impact globally. The chances recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting the identification melanoma. Despite their high performance, relying solely on an image classifier undermines credibility application makes it difficult to understand rationale behind model's predictions highlighting need Explainable AI (XAI). This study provides survey cancer using DL techniques utilized studies from 2017 2024. Compared existing studies, authors address latest related covering several public datasets focusing segmentation, classification based convolutional neural networks vision transformers, explainability. analysis comparisons will be beneficial researchers developers this area, identify suitable used automated classification. Thereby, findings can implement support applications advancing diagnosis process.

Язык: Английский

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MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation

Sensors, Год журнала: 2024, Номер 24(23), С. 7473 - 7473

Опубликована: Ноя. 23, 2024

Accurate polyp image segmentation is of great significance, because it can help in the detection polyps. Convolutional neural network (CNN) a common automatic method, but its main disadvantage long training time. Transformer another method that be adapted to by employing self-attention mechanism, which essentially assigns different importance weights each piece information, thus achieving high computational efficiency during segmentation. However, potential drawback with risk information loss. The study reported this paper employed well-known hybridization principle propose combine CNN and retain strengths both. Specifically, applied early colonic polyps implement model called MugenNet for We conducted comprehensive experiment compare other models on five publicly available datasets. An ablation was as well. experimental results showed achieve mean Dice 0.714 ETIS dataset, optimal performance dataset compared models, an inference speed 56 FPS. overall outcome optimally two methods machine learning are complementary other.

Язык: Английский

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U-MGA: A Multi-Module Unet Optimized with Multi-Scale Global Attention Mechanisms for Fine-Grained Segmentation of Cultivated Areas

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 760 - 760

Опубликована: Фев. 22, 2025

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture distribution in remote sensing images make it susceptible interference from other cover types, such as water bodies, roads, buildings, complicating accurate identification. Building on previous research, this study proposes an efficient lightweight CNN-based network, U-MGA, address the challenges feature similarity between arable non-arable areas, insufficient fine-grained extraction, underutilization multi-scale information. Specifically, Multi-Scale Adaptive Segmentation (MSAS) designed during extraction phase provide multi-feature information, supporting model’s reconstruction stage. In phase, introduction Contextual Module (MCM) Group Aggregation Bridge (GAB) significantly enhances efficiency accuracy utilization. The experiments conducted dataset based GF-2 imagery publicly available show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), Kappa coefficient). Thus, provides precise solution for recognition task, which significant importance resource monitoring ecological environmental protection.

Язык: Английский

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PACAF-Net: pixel shuffling based fiderality-preserved up/downsampling and adaptive cross-attention fusion for effective medical image segmentation

Signal Image and Video Processing, Год журнала: 2025, Номер 19(5)

Опубликована: Март 3, 2025

Язык: Английский

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