GMSRF-Net: An improved generalizability with global multi-scale residual
fusion network for polyp segmentation
- URL: http://arxiv.org/abs/2111.10614v1
- Date: Sat, 20 Nov 2021 15:41:59 GMT
- Title: GMSRF-Net: An improved generalizability with global multi-scale residual
fusion network for polyp segmentation
- Authors: Abhishek Srivastava, Sukalpa Chanda, Debesh Jha, Umapada Pal, and
Sharib Ali
- Abstract summary: Colonoscopy is a gold standard procedure but is highly operator-dependent.
Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate.
Computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy.
- Score: 12.086664133486144
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Colonoscopy is a gold standard procedure but is highly operator-dependent.
Efforts have been made to automate the detection and segmentation of polyps, a
precancerous precursor, to effectively minimize missed rate. Widely used
computer-aided polyp segmentation systems actuated by encoder-decoder have
achieved high performance in terms of accuracy. However, polyp segmentation
datasets collected from varied centers can follow different imaging protocols
leading to difference in data distribution. As a result, most methods suffer
from performance drop and require re-training for each specific dataset. We
address this generalizability issue by proposing a global multi-scale residual
fusion network (GMSRF-Net). Our proposed network maintains high-resolution
representations while performing multi-scale fusion operations for all
resolution scales. To further leverage scale information, we design cross
multi-scale attention (CMSA) and multi-scale feature selection (MSFS) modules
within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS
demonstrate improved generalizability of the network. Experiments conducted on
two different polyp segmentation datasets show that our proposed GMSRF-Net
outperforms the previous top-performing state-of-the-art method by 8.34% and
10.31% on unseen CVC-ClinicDB and unseen Kvasir-SEG, in terms of dice
coefficient.
Related papers
- ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation.
SAM's Transformer-based structure prioritizes global and low-frequency information.
CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
arXiv Detail & Related papers (2024-06-30T14:55:32Z) - Dual-scale Enhanced and Cross-generative Consistency Learning for
Semi-supervised Polyp Segmentation [52.06525450636897]
Automatic polyp segmentation plays a crucial role in the early diagnosis and treatment of colorectal cancer.
Existing methods rely heavily on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations.
We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised polyp (DEC-Seg) from colonoscopy images.
arXiv Detail & Related papers (2023-12-26T12:56:31Z) - Edge-aware Feature Aggregation Network for Polyp Segmentation [40.3881565207086]
In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation.
EFA-Net can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation.
Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.
arXiv Detail & Related papers (2023-09-19T11:09:38Z) - TransNetR: Transformer-based Residual Network for Polyp Segmentation
with Multi-Center Out-of-Distribution Testing [2.3293678240472517]
We propose a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR) for colon polyp segmentation.
TransNetR is an encoder-decoder network that consists of a pre-trained ResNet50 as the encoder, three decoder blocks, and an upsampling layer at the end of the network.
It obtains a high dice coefficient of 0.8706 and a mean Intersection over union of 0.8016 and retains a real-time processing speed of 54.60 on the Kvasir-SEG dataset.
arXiv Detail & Related papers (2023-03-13T19:11:17Z) - Automatic Polyp Segmentation via Multi-scale Subtraction Network [100.94922587360871]
In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer.
Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder.
We propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image.
arXiv Detail & Related papers (2021-08-11T07:54:07Z) - CSRNet: Cascaded Selective Resolution Network for Real-time Semantic
Segmentation [18.63596070055678]
We propose a light Cascaded Selective Resolution Network (CSRNet) to improve the performance of real-time segmentation.
The proposed network builds a three-stage segmentation system, which integrates feature information from low resolution to high resolution.
Experiments on two well-known datasets demonstrate that the proposed CSRNet effectively improves the performance for real-time segmentation.
arXiv Detail & Related papers (2021-06-08T14:22:09Z) - MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image
Segmentation [10.979393806308648]
We propose a novel architecture called MSRF-Net, which is specially designed for medical image segmentation tasks.
MSRF-Net is able to exchange multi-scale features of varying receptive fields using a dual-scale dense fusion block (DSDF)
Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion.
arXiv Detail & Related papers (2021-05-16T15:19:56Z) - Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral
Super-Resolution [79.97180849505294]
We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI.
Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
arXiv Detail & Related papers (2020-07-10T08:08:20Z) - PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
We first aggregate the features in high-level layers using a parallel partial decoder (PPD)
In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
arXiv Detail & Related papers (2020-06-13T08:13:43Z) - MS-Net: Multi-Site Network for Improving Prostate Segmentation with
Heterogeneous MRI Data [75.73881040581767]
We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations.
Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
arXiv Detail & Related papers (2020-02-09T14:11:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.