Search Results for author:
Found 17 papers, 1 papers with code
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data
The proposed multi-scale nested decoder structure allows the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features.
Representing Additive Gaussian Processes by Sparse Matrices
We show how to use these sparse formulas to generalize back-fitting-based algorithms to efficiently compute the posterior mean, posterior variance, log-likelihood, and gradient of these three functions for additive GPs, all in $O(n \log n)$ time.
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Histopathological Image Classification
Image Classification
+3
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Mat\'ern correlations whose smoothness parameter $\nu$ is a half-integer.
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension
To enhance the interpretability of the model, we propose the semantic role relation table, which represents the semantic relation of the sentence itself and the semantic relations among sentences.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer
In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
