An important indicator of cervical cancer diagnosis is to calculate the proportion of diseased cells and cancer cells, so it is necessary to segment cells and judge the cell status. The existing methods are difficult to deal with the segmentation of overlapping cells. In order to solve this problem, we put forward such a hypothesis by reading a large number of literature, that is, image segmentation and edge measurement tasks have unity in high-level features. To prove this hypothesis, in this paper, we focus on the complementary between overlapping cervical cell edge information and cell object information to get higher accuracy cell edge detection results. Specifically, we present a joint multi-task learning framework for overlapping cell edge detection by the mask guidance pyramid network. The main component of the framework is the Mask Guidance Module (MGM), which integrates two tasks and stores the shared latent semantics to interact in the two tasks. For semantic edge detection, we propose the novel Refinement Aggregated Module (RAM) fusion to promote semantic edges. Finally, to improve the edge pixel accuracy, the edge consistency constraint loss function is introduced to our model training. Our extensive experiments have proved that our method outperforms other edge detection efforts.

/pdf/mask-guidance-pyramid-network-for-overlapping-cervical-cell-v8ya7sra.pdf

Mask Guidance Pyramid Network for Overlapping Cervical Cell Edge Detection

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown a promise. However, implicit cross-task consistency learning in multitask networks is limited. To address this, we propose a novel “decoupled cross-task consistency loss” that explicitly enhances cross-task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4% mIoU/46.2 FPS on Cityscapes, showcasing real-time inference capabilities at full resolution. With multiscale inference, performance is further enhanced to 77.8%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multitask learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation. 

TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency

Semantic segmentation is a classic computer vision problem dedicated to labeling each pixel with its corresponding category. As a basic task for advanced tasks such as industrial quality inspection, remote sensing information extraction, medical diagnostic aid, and autonomous driving, semantic segmentation has been developed for a long time in combination with deep learning, and a lot of works have been accumulated. However, neither the classic FCN-based works nor the popular Transformer-based works have attained fine-grained localization of pixel labels, which remains the main challenge in this field. Recently, with the popularity of autonomous driving, the segmentation of road scenes has received increasing attention. Based on the cross-task consistency theory, we incorporate edge priors into semantic segmentation tasks to obtain better results. The main contribution is that we provide a model-agnostic method that improves the accuracy of semantic segmentation models with zero extra inference runtime overhead, verified on the datasets of road and non-road scenes. From our experimental results, our method can effectively improve semantic segmentation accuracy. 

/pdf/trianglenet-edge-prior-augmented-network-for-semantic-3ee521wm.pdf

TriangleNet: Edge Prior Augmented Network for Semantic Segmentation
  through Cross-Task Consistency

Semantic segmentation is a classic computer vision problem ded-icated to labeling each pixel with its corresponding category. As a basic task for advanced tasks such as industrial quality inspection, remote sensing information extraction, medical diagnostic aid, and autonomous driving, semantic segmentation has been developed for a long time in combination with deep learning, and a lot of works have been accumulated. However, neither the classic FCN-based works nor the popular Transformer-based works have attained ﬁne-grained local-ization of pixel labels, which remains the main challenge in this ﬁeld. Recently, with the popularity of autonomous driving, the segmentation of road scenes has received increasing attention. Based on the cross-task consistency theory, we incorporate edge priors into semantic segmentation tasks to obtain better results. The main contribution is that we provide a model-agnostic method that improves the accuracy of semantic segmentation models with zero extra inference runtime overhead, veriﬁed on the datasets of road and non-road scenes. From our experimental results, our method can eﬀectively improve semantic segmentation accuracy.

pdf/trianglenet-edge-prior-augmented-network-for-semantic-3lhp83ru.pdf

Semantic segmentation is a challenging task which can be formulated as a pixel-wise classification problem. Most FCN-based methods of semantic segmentation apply simple bilinear up-sampling to recover the final pixel-wise prediction, which may lead to misclassification near the object edges. To solve this problem, we focus on the supplementary spatial details of semantic segmentation using edge information. We present an approach to incorporate the relevant auxiliary edge information to semantic segmentation features. By applying the explicit supervision of semantic boundary using intermediate features, the multi-tasks network learns features with strong inter-class distinctive ability. The attention-based feature fusion module fuses the high-resolution edge features with wide-receptive-field semantic features to sufficiently leverage the complementary information. Experiments on the Cityscapes dataset show the effectiveness of fusing intermediate edge information.

Auxiliary Edge Detection for Semantic Image Segmentation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

He Xu

Author Tools

Chat about Author

Papers

Auxiliary Edge Detection for Semantic Image Segmentation