311 research outputs found
Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
Recent advancements in artificial intelligence (AI) have facilitated its
widespread adoption in primary medical services, addressing the demand-supply
imbalance in healthcare. Vision Transformers (ViT) have emerged as
state-of-the-art computer vision models, benefiting from self-attention
modules. However, compared to traditional machine-learning approaches,
deep-learning models are complex and are often treated as a "black box" that
can cause uncertainty regarding how they operate. Explainable Artificial
Intelligence (XAI) refers to methods that explain and interpret machine
learning models' inner workings and how they come to decisions, which is
especially important in the medical domain to guide the healthcare
decision-making process. This review summarises recent ViT advancements and
interpretative approaches to understanding the decision-making process of ViT,
enabling transparency in medical diagnosis applications
Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images
Breast cancer (BC) significantly contributes to cancer-related mortality in
women, underscoring the criticality of early detection for optimal patient
outcomes. A mammography is a key tool for identifying and diagnosing breast
abnormalities; however, accurately distinguishing malignant mass lesions
remains challenging. To address this issue, we propose a novel deep learning
approach for BC screening utilizing mammography images. Our proposed model
comprises three distinct stages: data collection from established benchmark
sources, image segmentation employing an Atrous Convolution-based Attentive and
Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via
an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet
(ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN
models are optimised using the Modified Mussel Length-based Eurasian
Oystercatcher Optimization (MML-EOO) algorithm. Performance evaluation,
leveraging multiple metrics, is conducted, and a comparative analysis against
conventional methods is presented. Our experimental findings reveal that the
proposed BC detection framework attains superior precision rates in early
disease detection, demonstrating its potential to enhance mammography-based
screening methodologies.Comment: 22 pages, 17 figures, 4 Tables and Appendix A: Supplementary Materia
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
Histopathological images are the gold standard for diagnosing liver cancer.
However, the accuracy of fully digital diagnosis in computational pathology
needs to be improved. In this paper, in order to solve the problem of
multi-label and low classification accuracy of histopathology images, we
propose a locally deep convolutional Swim framework (LDCSF) to classify
multi-label histopathology images. In order to be able to provide local field
of view diagnostic results, we propose the LDCSF model, which consists of a
Swin transformer module, a local depth convolution (LDC) module, a feature
reconstruction (FR) module, and a ResNet module. The Swin transformer module
reduces the amount of computation generated by the attention mechanism by
limiting the attention to each window. The LDC then reconstructs the attention
map and performs convolution operations in multiple channels, passing the
resulting feature map to the next layer. The FR module uses the corresponding
weight coefficient vectors obtained from the channels to dot product with the
original feature map vector matrix to generate representative feature maps.
Finally, the residual network undertakes the final classification task. As a
result, the classification accuracy of LDCSF for interstitial area, necrosis,
non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
Finally, we use the results of multi-label pathological image classification to
calculate the tumor-to-stromal ratio, which lays the foundation for the
analysis of the microenvironment of liver cancer histopathological images.
Second, we released a multilabel histopathology image of liver cancer, our code
and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202
Learning where to see : a novel attention model for automated immunohistochemical scoring
Estimatingover-amplification of human epidermal growth factor receptor2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the subimage patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model out performs other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images
Nucleus segmentation : towards automated solutions
Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe
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