UniCell: Universal Cell Nucleus Classification via Prompt Learning

• URL: http://arxiv.org/abs/2402.12938v1
• Date: Tue, 20 Feb 2024 11:50:27 GMT
• Title: UniCell: Universal Cell Nucleus Classification via Prompt Learning
• Authors: Junjia Huang, Haofeng Li, Xiang Wan, Guanbin Li
• Abstract summary: We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
• Score: 76.11864242047074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

Related papers

• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Prompt-based Grouping Transformer for Nucleus Detection and Classification [70.55961378096116]
  nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use of the semantic similarity between nuclei and their grouping features. We propose a novel end-to-end nuclei detection and classification framework based on a grouping transformer-based classifier.
  arXiv  Detail & Related papers  (2023-10-22T04:50:48Z)
• Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification [2.5016806673359393]
  We propose a method to train deep neural networks (DNNs) on multiple datasets where the set of classes are related but not the same. Specifically, our method is designed to utilize a coarse-to-fine class hierarchy, where the set of classes labeled and annotated in a dataset can be at any level of the hierarchy. Our results demonstrate that segmentation and classification metrics for the class set used by the test split of a dataset can improve by pre-training on another dataset.
  arXiv  Detail & Related papers  (2023-10-05T06:56:54Z)
• Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing [0.0]
  This paper focuses on a weakly-supervised training setting for single-cell segmentation models. Of more interest is a proposed selflearning method called collaborative knowledge sharing. This strategy achieves selflearning by sharing knowledge between a principal model and a very lightweight collaborator model.
  arXiv  Detail & Related papers  (2023-04-20T23:22:41Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• CCRL: Contrastive Cell Representation Learning [0.0]
  We propose Contrastive Cell Representation Learning (CCRL) model for cell identification in H&E slides. We show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types.
  arXiv  Detail & Related papers  (2022-08-12T18:12:03Z)
• Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
  We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
  arXiv  Detail & Related papers  (2022-07-04T07:05:06Z)
• PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification [23.466331358975044]
  We study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin stained histopathology data. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types.
  arXiv  Detail & Related papers  (2021-11-01T08:29:40Z)
• A Meta-embedding-based Ensemble Approach for ICD Coding Prediction [64.42386426730695]
  International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles.
  arXiv  Detail & Related papers  (2021-02-26T17:49:58Z)

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.