Representation Learning of Histopathology Images using Graph NeuralNetworks

Mohammed Adnan1,2, Shivam Kalra1, Hamid R. Tizhoosh1,2

1Kimia Lab, University of Waterloo, Canada2Vector Institute, Canada

{m7adnan,shivam.kalra,tizhoosh}@uwaterloo.ca

Abstract

Representation learning for Whole Slide Images (WSIs)is pivotal in developing image-based systems to achievehigher precision in diagnostic pathology. We propose atwo-stage framework for WSI representation learning. Wesample relevant patches using a color-based method anduse graph neural networks to learn relations among sam-pled patches to aggregate the image information into a sin-gle vector representation. We introduce attention via graphpooling to automatically infer patches with higher rele-vance. We demonstrate the performance of our approachfor discriminating two sub-types of lung cancers, Lung Ade-nocarcinoma (LUAD) & Lung Squamous Cell Carcinoma(LUSC). We collected 1,026 lung cancer WSIs with the40× magnification from The Cancer Genome Atlas (TCGA)dataset, the largest public repository of histopathology im-ages and achieved state-of-the-art accuracy of 88.8% andAUC of 0.89 on lung cancer sub-type classification by ex-tracting features from a pre-trained DenseNet model.

1. Introduction

Large archives of digital scans in pathology are graduallybecoming a reality. The amount of information stored insuch archives is both impressive and overwhelming. How-ever, there is no convenient provision to access this storedknowledge and make it available to pathologists for diag-nostic, research, and educational purposes. This limitationis mainly due to the lack of techniques for representingWSIs. The characterization of WSIs offers various chal-lenges in terms of image size, complexity and color, anddefinitiveness of diagnosis at the pathology level, also thesheer amount of effort required to annotate a large numberof images. These challenges necessitate inquiry into moreeffective ways of representing WSIs.

The recent success of “deep learning” has openedpromising horizons for digital pathology. This has moti-

vated both AI experts and pathologists to work togetherin order to create novel diagnostic algorithms. This op-portunity has become possible with the widespread adop-tion of digital pathology, which has increased the demandfor effective and efficient analysis of Whole Slide Images(WSIs). Deep learning is at the forefront of computer vi-sion, showcasing significant improvements over conven-tional methodologies on visual understanding. However,each WSI consist of billions of pixel and therefore, deepneural networks cannot process them. Most of the recentwork analyzes WSIs at the patch level, which requires man-ual delineation from an expert. Therefore, the feasibilityof such approaches is rather limited for larger archives ofWSIs. Moreover, most of the time, labels are available forthe entire WSI, and not for individual patches. To learn arepresentation of a WSI, it is, therefore, necessary to lever-age the information present in all patches. Hence, multipleinstance learning (MIL) is a promising approach for learn-ing WSI representation.

MIL is a type of supervised learning approach whichuses a set of instances known as a bag. Each bag has anassociated label instead of individual instances. MIL is thusa natural candidate for learning WSI representation. We ex-plore the application of graph neural networks for MIL. Wepropose a framework that models a WSI as a fully con-nected graph to extract its representation. The proposedmethod processes the entire WSI at the highest magnifi-cation level; it requires a single label of the WSI with-out any patch-level annotations. Furthermore, modelingWSIs as fully-connected graphs enhance the interpretabil-ity of the final representation. We treat each instance asa node of the graph to learn relations among nodes in anend-to-end fashion. Thus, our proposed method not onlylearns the representation for a given WSI but also modelsrelations among different patches. We explore our methodfor classifying two subtypes of lung cancer, Lung Adeno-carcinoma (LUAD) and Lung Squamous Cell Carcinoma(LUSC). LUAD and LUSC are the most prevalent subtypesof lung cancer, and their distinction requires visual inspec-

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tion by an experienced pathologist. In this study, we usedWSIs from the largest publicly available dataset, The Can-cer Genome Atlas (TCGA) [30], to train the model for lungcancer subtype classification. We propose a novel architec-ture using graph neural networks for learning WSI repre-sentation by modeling the relation among different patchesin the form of the adjacency matrix. Our proposed methodachieved an accuracy of 89% and 0.93 AUC. The contribu-tions of the paper are 3-folds, i) a graph-based method forrepresentation learning of WSIs, and ii) a novel adjacencylearning layer for learning connections within nodes in anend-to-end manner, and iii) visualizing patches which aregiven higher importance by the network for the prediction.

The paper is structured as follows: Section 2 briefly cov-ers the related work. Section 3 discusses Graph ConvolutionNeural Networks (GCNNs) and deep sets. Section 4 ex-plains the approach, and experiments & results are reportedin Section 5.

2. Related WorkWith an increase in the workload of pathologists, there

is a clear need to integrate CAD systems into pathologyroutines [21, 24, 23, 10]. Researchers in both image anal-ysis and pathology fields have recognized the importanceof quantitative image analysis by using machine learning(ML) techniques [10]. The continuous advancement ofdigital pathology scanners and their proliferation in clinicsand laboratories have resulted in a substantial accumulationof histopathology images, justifying the increased demandfor their analysis to improve the current state of diagnosticpathology [24, 21].

Histopathology Image Representation. To develop CADsystems for histopathology images (WSIs), it is crucial totransform WSIs into feature vectors that capture their di-agnostic semantics. There are two main methods for char-acterizing WSIs [2]. The first method is called sub-settingmethod, which considers a small section of a large pathol-ogy image as an essential part such that the processing of asmall subset substantially reduces processing time. A ma-jority of research studies in the literature have used the sub-setting method because of its speed and accuracy. How-ever, this method requires expert knowledge and interven-tion to extract the proper subset. On the other hand, thetiling method segments images into smaller and controllablepatches (i.e., tiles) and tries to process them against eachother [11], which requires a more automated approach. Thetiling method can benefit from MIL; for example, Ilse etal. [15] used MIL with attention to classify breast and coloncancer images.

Due to the recent success of artificial intelligence (AI)in computer vision applications, many researchers andphysicians expect that AI would be able to assist physicians

in many tasks in digital pathology. However, digitalpathology images are difficult to use for training neuralnetworks. A single WSI is a gigapixel file and exhibits highmorphological heterogeneity and may as well as containdifferent artifacts. All in all, this impedes the common useof deep learning [6].

Multiple Instance Learning (MIL). MIL algorithms as-sign a class label to a set of instances rather than to individ-ual instances. The individual instance labels are not neces-sarily important, depending on the type of algorithm and itsunderlying assumptions. [3]. Learning representation forhistopathology images can be formulated as a MIL prob-lem. Due to the intrinsic ambiguity and difficulty in obtain-ing human labeling, MIL approaches have their particularadvantages in automatically exploiting the fine-grained in-formation and reducing efforts of human annotations. Isleet al. used MIL for digital pathology and introduces a dif-ferent variety of MIL pooling functions [16]. Sudarshanet al. used MIL for histopathological breast cancer imageclassification [26]. Permutation invariant operator for MILwas introduced by Tomczak et al. and successfully appliedto digital pathology images [27]. Graph neural networks(GNNs) have been used for MIL applications because oftheir permutation invariant characteristics. Tu et al. showedthat GNNs can be used for MIL, where each instance actsas a node in a graph [28]. Anand et al. proposes a GNNbased approach to classify WSIs represented by graphs ofits constituent cells [1].

3. BackgroundGraph Representation. A graph can be fully representedby its node list V and adjacency matrix A. Graphs canmodel many types of relations and processes in physical,biological, social, and information systems. A connectionbetween two nodes Vi and Vj is represented using an edgeweighted by aij .

Graph Convolution Neural Networks (GCNNs). GCNNsgeneralize the operation of convolution from grid data tograph data. A GCNN takes a graph as an input and trans-forms it into another graph as the output. Each feature nodein the output graph is computed by aggregating features ofthe corresponding nodes and their neighboring nodes in theinput graph. Like CNNs, GCNNs can stack multiple layersto extract high-level node representations. Dependingupon the method for aggregating features, GCNNs can bedivided into two categories, namely spectral-based andspatial-based. Spectral-based approaches define graphconvolutions by introducing filters from the perspectiveof graph signal processing. Spectral convolutions aredefined as the multiplication of a node signal by a kernel.This is similar to the way convolutions operate on an

image, where a pixel value is multiplied by a kernel value.Spatial-based approaches formulate graph convolutions asaggregating feature information from neighbors. Spatialgraph convolution learns the aggregation function, which ispermutation invariant to the ordering of the node.

ChebNet. It was introduced by Defferrard et al. [5]. Spec-tral convolutions on graphs are defined as the multiplicationof a signal x ∈ RN (a scalar for every node) with a filterg(θ) = diag(θ) parameterized by θ ∈ RN in the Fourierdomain, i.e.,

gθ ~ x = UgθUTx,

where U is the matrix of eigenvectors of the normalizedgraph Laplacian L = IN − D−

12AD−

12 . This equation

is computationally expensive to calculate as multiplicationwith the eigenvector matrix U is O(N2). Hammond etal. [13] suggested that that gθ can be well-approximated bya truncated expansion in terms of Chebyshev polynomialsTk(x), i.e,

gθ′(Λ) ≈K∑k=0

θ′Tk(Λ).

The kernels used in ChebNet are made of Chebyshevpolynomials of the diagonal matrix of Laplacian eigenval-ues. ChebNet uses kernel made of Chebyshev polynomials.Chebyshev polynomials are a type of orthogonal polynomi-als with properties that make them very good at tasks likeapproximating functions.

GraphSAGE. It was introduced by Hamilton et al. [12].GraphSAGE learns aggregation functions that can inducethe embedding of a new node given its features and neigh-borhood. This is called inductive learning. GraphSAGEis a framework for inductive representation learning onlarge graphs that can generate low-dimensional vectorrepresentations for nodes and is especially useful for graphsthat have rich node attribute information. It is much fasterto create embeddings for new nodes with GraphSAGE.

Graph Pooling Layers. Similar to CNNs, pooling layersin GNNs downsample node features by pooling operation.We experimented with Global Attention Pooling, MeanPooling, Max Pooling, and Sum Pooling. Global AttentionPooling [22] was introduced by Li et al. and uses softattention mechanism to decide which nodes are relevant tothe current graph-level task and gives the pooled featurevector from all the nodes.

Universal Approximator for Sets. We use results fromDeep Sets [32] to get the global context of the set of patchesrepresenting WSI. Zaheer et al. proved in [32] that any setcan be approximated by ρ

∑(φ(x)) where ρ and φ are some

function, and x is the element in the set to be approximated.

4. MethodThe proposed method for representing a WSI has two

stages, i) sampling important patches and modeling theminto a fully-connected graph, and ii) converting the fully-connected graph into a vector representation for classifica-tion or regression purposes. These two stages can be learnedend-to-end in a single training loop. The major novelty ofour method is the learning of the adjacency matrix that de-fines the connections within nodes. The overall proposedmethod is shown in Figure 1 and Figure 2. The method canbe summarized as follows.

1. The important patches are sampled from a WSI usinga color-based method described in [18]. A pre-trainedCNN is used to extract features from all the sampledpatches.

2. The given WSI is then modeled as a fully-connectedgraph. Each node is connected to every other nodebased on the adjacency matrix. The adjacency matrixis learned end-to-end using Adjacency Learning Layer.

3. The graph is then passed through a Graph ConvolutionNetwork followed by a graph pooling layer to producethe final vector representation for the given WSI.

The main advantage of the method is that it processesentire WSIs. The final vector representation of a WSI canbe used for various tasks—classification (prediction cancertype), search (KNN search), or regression (tumor grading,survival prediction) and others.

Patch Selection and Feature Extraction. We used themethod for patch selection proposed in [18]. Every WSIcontains a bright background that generally contains irrele-vant (non-tissue) pixel information. We removed non-tissueregions using color thresholds. Segmented tissue is thendivided into patches. All patches are grouped into a pre-setnumber of categories (classes) via a clustering method. Aportion of all clustered patches (e.g., 10%) are randomlyselected within each class. Each patch obtained after patchselection is fed into a pre-trained DenseNet [14] for featureextraction. We further feed these features to trainable fullyconnected layers and obtain final feature vectors each ofdimension 1024 representing patches.

Graph Representation of WSI. We propose a novelmethod for learning WSI representation using GCNNs.Each WSI is converted to a fully-connected graph, whichhas the following two components.

1. Nodes V : Each patch feature vector represents a nodein the graph. The feature for each node is the same asthe feature extracted for the corresponding patch.

Figure 1: Transforming a WSI to a fully-connected graph. A WSI is represented as a graph with its nodes correspondingto distinct patches from the WSI. A node feature (a blue block beside each node) is extracted by feeding the associatedpatch through a deep network. A single context vector, summarizing the entire graph is computed by pooling all the nodefeatures. The context vector is concatenated with each node feature, subsequently fed into adjacent learning block. Theadjacent learning block uses a series of dense layers and cross-correlation to calculate the adjacency matrix. The computedadjacency matrix is used to produce the final fully-connected graph. In the figure, the thickness of the edge connecting twonodes corresponds to the value in the adjacency matrix.

2. Adjacency Matrix A: Patch features are used to learnthe A via adjacency learning layer.

Adjacency Learning Layer. Connections between nodesV are expressed in the form of the adjacency matrix A.Our model learns the adjacency matrix in an end-to-endfashion in contrast to the method proposed in [28] thatthresholds the `2 distance on pre-computed features. Ourproposed method also uses global information about thepatches while calculating the adjacency matrix. The ideabehind using the global context is that connection betweentwo same nodes/patches can differ for different WSIs; there-fore, elements in the adjacency matrix should depend notonly on the relation between two patches but also on theglobal context of all the patches.

1. Let W be a WSI and w1, w2, . . . wn be its patches.Each patch wi is passed through a feature extractionlayer to obtain corresponding feature representationxi.

2. We use the theorem by Zaheer et al. [32] to obtain theglobal context from the features xi. Feature vectorsfrom all patches in the given WSI are pooled using apooling function φ to get the global context vector c.Mathematically,

c = φ(x1, x2, . . . , xn). (1)

Zaheer et al. showed that such a function can be usedas an universal set approximator.

3. The global context vector c is then concatenated toeach feature vector xi to obtain concatenated feature

vector x′i which is passed through MLP layers to ob-tain new feature vector x∗i · x∗i are the new featuresthat contain information about the patch as well as theglobal context.

4. Features x∗i are stacked together to form a feature ma-trix X∗ and passed through a cross-correlation layer toobtain adjacency matrix denoted by A

n×nwhere each

element aij in A shows the degree of correlation be-tween the patches wi and wj . We use aij to representthe edge weights between different nodes in the fullyconnected graph representation of a given WSI.

Graph Convolution Layers. Once we implemented thegraph representation of the WSI, we experimented with twotypes of GCNN: ChebNets and GraphSAGE Convolution,which are spectral and spatial methods, respectively. Eachhidden layer in GCNN models the interaction betweennodes and transforms the feature into another feature space.Finally, we have a graph pooling layer that transforms nodefeatures into a single vector representation. Thus, a WSIcan now be represented by a condensed vector, which canbe further used to do other tasks such as classification,image retrieval, etc.

General MIL Framework. Our proposed method can beused in any MIL framework. The general algorithm forsolving MIL problems is as follows:

1. Consider each instance as a node and its correspondingfeature as the node features.

2. The global context of the bag of instances is learned tocalculate the adjacency matrix A.

Figure 2: Classification of a graph representing a WSI. A fully connected graph representing a WSI is fed through a graphconvolution layer to transform it into another fully-connected graph. After a series of transformations, the nodes of the finalfully-connected graph are aggregated to a single condensed vector, which is fed to an MLP for classification purposes.

3. A fully connected graph is constructed with each in-stance as a node and aij in A representing the edgeweight between Vi and Vj .

4. Graph convolution network is used to learn the repre-sentation of the graph, which is passed through a graphpooling layer to get a single feature vector representingthe bag of instances.

5. The single feature vector from the graph can be usedfor classification or other learning tasks.

5. ExperimentsWe evaluated the performance of our model on two

datasets i) a popular benchmark dataset for MIL calledMUSK1 [7], and ii) 1026 lung slides from TCGAdataset [11]. Our proposed method achieved a state-of-the-art accuracy of 92.6% on the MUSK1 dataset. We furtherused our model to discriminate between two sub-types oflung cancer—Lung Adenocarcinoma (LUAD) and LungSquamous Cell Carcinoma (LUSC).

MUSK1 Dataset. It has 47 positive bags and 45 negativebags. Instances within a bag are different conformations ofa molecule. The task is to predict whether new moleculeswill be musks or non-musks. We performed 10 foldcross-validation five times with different random seeds.We compared our approach with various other works inliterature, as reported in Table 1. The miGraph [33] isbased on kernel learning on graphs converted from thebag of instances. The latter two algorithms, MI-Net [29],and Attention-MIL [15], are based on DNN and useeither pooling or attention mechanism to derive the bagembedding.

LUAD vs LUSC Classification. Lung Adenocarcinoma(LUAD) and Lung Squamous Cell Carcinoma (LUSC) aretwo main sub-types of non-small cell lung cancer (NSCLC)

Algorithm Accuracymi-Graph [33] 0.889MI-Net [29] 0.887MI-Net with DS [29] 0.894Attention-MIL [15] 0.892Attention-MIL with gating [15] 0.900Ming Tu et al. [28] 0.917Proposed Method 0.926

Table 1: Evaluation on MUSK1.

that account for 65-70% of all lung cancers [9]. Auto-mated classification of these two main subtypes of NSCLCis a crucial step to build computerized decision support andtriage systems.

We obtained 1,026 hematoxylin and eosin (H&E)stained permanent diagnostic WSIs from TCGA reposi-tory [11] encompassing LUAD and LUSC. We selectedrelevant patches from each WSI using a color-based patchselection algorithm described in [18, 19]. Furthermore,we extracted image features from these patches usingDenseNet [14]. Now, each bag in this scenario is a setof features labeled as either LUAD or LUSC. We trainedour model to classify bags as two cancer subtypes. Thehighest 5-fold classification AUC score achieved was0.92, and the average AUC across all folds was 0.89. Weperformed cross-validation across different patients, i.e.,training was performed using WSIs from a totally differentset of patients than the testing. The results are reportedin Table 2. We achieved state-of-the-art accuracy usingthe transfer learning scheme. In other words, we extractedpatch features from an existing pre-trained network, and thefeature extractor was not re-trained or fine-tuned during thetraining process. The Figure 5 shows the receiver operatingcurve (ROC) for one of the folds.

Inference. One of the primary obstacles for real-worldapplication of deep learning models in computer-aided

Figure 3: Six patches from two WSIs diagnosed with LUSC and LUAD, respectively. The six patches are selected, such thatthe first three (top row) are highly “attended” by the network, whereas the last three (bottom row) least attended. The firstpatch in the upper row is the most attended patch (more important) and the first patch in the lower row in the least attendedpatch (less important).

Algorithm AUCCoudray et al. [4] 0.85Khosravi et al. [20] 0.83Yu et al. [31] 0.75Proposed method 0.89

Table 2: Performance of various methods for LUAD/LUSCpredictions using transfer learning. Our results report theaverage of 5-fold accuracy values.

diagnosis is the black-box nature of the deep neuralnetworks. Since our proposed architecture uses GlobalAttention Pooling [22], we can visualize the importancethat our network gives to each patch for making the finalprediction. Such visualization can provide more insightto pathologists regarding the model’s internal decisionmaking. The global attention pooling layer learns to mappatches to “attention” values. The higher attention valuessignify that the model focuses more on those patches.We visualize the patches with high and low attentionvalues in Figure 3. One of the practical applications ofour approach would be for triaging. As new cases arequeued for an expert’s analysis, the CAD system couldhighlight the regions of interests and sort the cases basedon the diagnostic urgency. We observe that patches withhigher attention values generally contain more nuclei. Asmorphological features of nuclei are vitals for makingdiagnostic decisions [25], it is interesting to note thisproperty is learned on its own by the network. Figure 4

Figure 4: t-SNE visualization of feature vectors extractedafter the Graph Pooling layer from different WSIs. The twodistinct clusters for LUAD and LUSC demonstrate the effi-cacy of the proposed model for disease characterization inWSIs. The overlap of two clusters contain WSIs that aremorphologically and visually similar.

shows the t-SNE plot of features vectors for some of theWSIs. It shows the clear distinction between the two cancersubtypes, further favoring the robustness of our method.

Implementation Details. We used PyTorch Geometriclibrary to implement graph convolution networks [8]. Weused pre-trained DenseNet [14] to extract features fromhistopathology patches. We further feed DenseNet featuresthrough three dense layers with dropout (p = 0.5).

0.0 0.2 0.4 0.6 0.8 1.0False Positive Rate

0.0

0.2

0.4

0.6

0.8

1.0

True

Pos

itive

Rate

ROC curve (Area = 0.89)

Figure 5: The ROC curve of prediction.

Ablation Study. We tested our method with various differ-ent configurations for the TCGA dataset. We used two lay-ers in Graph Convolution Network—ChebNet and SAGEConvolution. We found that ChebNet outperforms SAGEConvolution and also results in better generalization. Fur-thermore, we experimented with different numbers of filtersin ChebNet, and also different pooling layers—global atten-tion, mean, max, and sum pooling. We feed the pooled rep-resentation to two fully connected Dense layers to get thefinal classification between LUAD and LUSC. All the dif-ferent permutations of various parameters result in 32 dif-ferent configurations, the results for all these configurationsare provided in Table 3. It should be noted that the resultsreported in the previous sections are based on Cheb-7 withmean pooling.

6. ConclusionThe accelerated adoption of digital pathology is coincid-

ing with and probably partly attributed to recent progress inAI applications in the field of pathology. This disruption inthe field of pathology offers a historic chance to find novelsolutions for major challenges in diagnostic histopathologyand adjacent fields, including biodiscovery. In this study,we proposed a technique for representing an entire WSIas a fully-connected graph. We used the graph convolu-tion networks to extract the features for classifying the lungWSIs into LUAD or LUSC. The results show the good per-formance of the proposed approach. Furthermore, the pro-posed method is explainable and transparent as we can useattention values and adjacency matrix to visualize relevantpatches.

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configuration mean attention max add

Cheb-7 0.8889 0.8853 0.7891 0.4929Cheb 3 BN 0.8771 0.8635 0.8471 0.5018Cheb 5 0.8762 0.8830 0.8750 0.5082Cheb 3 0.8752 0.8735 0.8702 0.5090Cheb 5 BN 0.8596 0.8542 0.7179 0.4707Cheb 7 BN 0.7239 0.6306 0.5618 0.4930SAGE CONV BN 0.6866 0.5848 0.6281 0.5787SAGE CONV 0.5784 0.6489 0.5389 0.5690

Table 3: Comparison of different network architecture and pooling method (attention, mean, max and sum pooling). BNstands for BatchNormalization [17], Cheb stands for Chebnet with corresponding filter size and SAGE stands for SAGEConvolution. The best performing configuration is Cheb-7 with mean pooling.

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