1,052 research outputs found
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image
classification, yet their pixel-level focus hampers explainability. Conversely,
emerging graph convolutional networks spotlight cell-level features and medical
implications. However, limited by their shallowness and suboptimal use of
high-dimensional pixel data, GCNs underperform in multi-class histopathological
image classification. To make full use of pixel-level and cell-level features
dynamically, we propose an asymmetric co-training framework combining a deep
graph convolutional network and a convolutional neural network for multi-class
histopathological image classification. To improve the explainability of the
entire framework by embedding morphological and topological distribution of
cells, we build a 14-layer deep graph convolutional network to handle cell
graph data. For the further utilization and dynamic interactions between
pixel-level and cell-level information, we also design a co-training strategy
to integrate the two asymmetric branches. Notably, we collect a private
clinically acquired dataset termed LUAD7C, including seven subtypes of lung
adenocarcinoma, which is rare and more challenging. We evaluated our approach
on the private LUAD7C and public colorectal cancer datasets, showcasing its
superior performance, explainability, and generalizability in multi-class
histopathological image classification
Deep learning features encode interpretable morphologies within histological images.
Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ( mones ) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
Accurate morphological classification of white blood cells (WBCs) is an
important step in the diagnosis of leukemia, a disease in which nonfunctional
blast cells accumulate in the bone marrow. Recently, deep convolutional neural
networks (CNNs) have been successfully used to classify leukocytes by training
them on single-cell images from a specific domain. Most CNN models assume that
the distributions of the training and test data are similar, i.e., that the
data are independently and identically distributed. Therefore, they are not
robust to different staining protocols, magnifications, resolutions, scanners,
or imaging protocols, as well as variations in clinical centers or patient
cohorts. In addition, domain-specific data imbalances affect the generalization
performance of classifiers. Here, we train a robust CNN for WBC classification
by addressing cross-domain data imbalance and domain shifts. To this end, we
use two loss functions and demonstrate the effectiveness on out-of-distribution
(OOD) generalization. Our approach achieves the best F1 macro score compared to
other existing methods, and is able to consider rare cell types. This is the
first demonstration of imbalanced domain generalization in hematological
cytomorphology and paves the way for robust single cell classification methods
for the application in laboratories and clinics.Comment: Published as a ICLR 2023 workshop paper: What do we need for
successful domain generalization
Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields
The high complexity of deep learning models is associated with the difficulty
of explaining what evidence they recognize as correlating with specific disease
labels. This information is critical for building trust in models and finding
their biases. Until now, automated deep learning visualization solutions have
identified regions of images used by classifiers, but these solutions are too
coarse, too noisy, or have a limited representation of the way images can
change. We propose a novel method for formulating and presenting spatial
explanations of disease evidence, called deformation field interpretation with
generative adversarial networks (DeFI-GAN). An adversarially trained generator
produces deformation fields that modify images of diseased patients to resemble
images of healthy patients. We validate the method studying chronic obstructive
pulmonary disease (COPD) evidence in chest x-rays (CXRs) and Alzheimer's
disease (AD) evidence in brain MRIs. When extracting disease evidence in
longitudinal data, we show compelling results against a baseline producing
difference maps. DeFI-GAN also highlights disease biomarkers not found by
previous methods and potential biases that may help in investigations of the
dataset and of the adopted learning methods.Comment: Accepted for MICCAI 202
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
This is not the Texture you are looking for! Introducing Novel Counterfactual Explanations for Non-Experts using Generative Adversarial Learning
With the ongoing rise of machine learning, the need for methods for
explaining decisions made by artificial intelligence systems is becoming a more
and more important topic. Especially for image classification tasks, many
state-of-the-art tools to explain such classifiers rely on visual highlighting
of important areas of the input data. Contrary, counterfactual explanation
systems try to enable a counterfactual reasoning by modifying the input image
in a way such that the classifier would have made a different prediction. By
doing so, the users of counterfactual explanation systems are equipped with a
completely different kind of explanatory information. However, methods for
generating realistic counterfactual explanations for image classifiers are
still rare. In this work, we present a novel approach to generate such
counterfactual image explanations based on adversarial image-to-image
translation techniques. Additionally, we conduct a user study to evaluate our
approach in a use case which was inspired by a healthcare scenario. Our results
show that our approach leads to significantly better results regarding mental
models, explanation satisfaction, trust, emotions, and self-efficacy than two
state-of-the art systems that work with saliency maps, namely LIME and LRP
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