Learning Binary Semantic Embedding for Histology Image Classification
and Retrieval
- URL: http://arxiv.org/abs/2010.03266v1
- Date: Wed, 7 Oct 2020 08:36:44 GMT
- Title: Learning Binary Semantic Embedding for Histology Image Classification
and Retrieval
- Authors: Xiao Kang, Xingbo Liu, Xiushan Nie, Yilong Yin
- Abstract summary: We propose a novel method for Learning Binary Semantic Embedding (LBSE)
Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images.
Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
- Score: 56.34863511025423
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the development of medical imaging technology and machine learning,
computer-assisted diagnosis which can provide impressive reference to
pathologists, attracts extensive research interests. The exponential growth of
medical images and uninterpretability of traditional classification models have
hindered the applications of computer-assisted diagnosis. To address these
issues, we propose a novel method for Learning Binary Semantic Embedding
(LBSE). Based on the efficient and effective embedding, classification and
retrieval are performed to provide interpretable computer-assisted diagnosis
for histology images. Furthermore, double supervision, bit uncorrelation and
balance constraint, asymmetric strategy and discrete optimization are
seamlessly integrated in the proposed method for learning binary embedding.
Experiments conducted on three benchmark datasets validate the superiority of
LBSE under various scenarios.
Related papers
- Brain-Adapter: Enhancing Neurological Disorder Analysis with Adapter-Tuning Multimodal Large Language Models [30.044545011553172]
This paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge.
Experiments demonstrated the effectiveness of our approach in integrating multimodal data to significantly improve the diagnosis accuracy without high computational costs.
arXiv Detail & Related papers (2025-01-27T18:20:49Z) - Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis [0.0]
Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets.
We investigated the feasibility of replacing conventional training procedures with an embedding-based approach.
arXiv Detail & Related papers (2024-12-12T16:59:37Z) - Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning [9.902648398258117]
This paper proposes a novel Cross-Graph Modal Contrastive Learning framework for multimodal structured data to improve medical image classification.
The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset.
Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction.
arXiv Detail & Related papers (2024-10-23T01:25:25Z) - MLIP: Enhancing Medical Visual Representation with Divergence Encoder
and Knowledge-guided Contrastive Learning [48.97640824497327]
We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning.
Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge.
Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
arXiv Detail & Related papers (2024-02-03T05:48:50Z) - Learned Image resizing with efficient training (LRET) facilitates
improved performance of large-scale digital histopathology image
classification models [0.0]
Histologic examination plays a crucial role in oncology research and diagnostics.
Current approaches to training deep convolutional neural networks (DCNN) result in suboptimal model performance.
We introduce a novel approach that addresses the main limitations of traditional histopathology classification model training.
arXiv Detail & Related papers (2024-01-19T23:45:47Z) - LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical
Imaging via Second-order Graph Matching [59.01894976615714]
We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets.
We have collected approximately 1.3 million medical images from 55 publicly available datasets.
LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
arXiv Detail & Related papers (2023-06-20T22:21:34Z) - Learning Discriminative Representation via Metric Learning for
Imbalanced Medical Image Classification [52.94051907952536]
We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations.
Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
arXiv Detail & Related papers (2022-07-14T14:57:01Z) - Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence
Tomography Classification [0.0]
We propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography.
We create a medical diagnostic attribute dataset to improve disease classification using OCT.
arXiv Detail & Related papers (2022-03-20T18:37:20Z) - 2021 BEETL Competition: Advancing Transfer Learning for Subject
Independence & Heterogenous EEG Data Sets [89.84774119537087]
We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI)
Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects.
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
arXiv Detail & Related papers (2022-02-14T12:12:20Z) - Explaining Clinical Decision Support Systems in Medical Imaging using
Cycle-Consistent Activation Maximization [112.2628296775395]
Clinical decision support using deep neural networks has become a topic of steadily growing interest.
clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend.
We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
arXiv Detail & Related papers (2020-10-09T14:39:27Z)
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.