TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Authors: Zhuchen Shao
Hao Bian
Yang Chen
Yifeng Wang
Jian Zhang
Xiangyang Ji
Yongbing Zhang
Venue: NeurIPS 2021 Poster (2021)
Keywords: Whole slide image
Weakly supervised classification
Multiple instance learning
Transformer

Abstract: Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided a proof for convergence. Based on this framework, we devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL can effectively deal with unbalanced/balanced and binary/multiple classification with great visualization and interpretability. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence compared with state-of-the-art methods. The test AUC for the binary tumor classification can be up to 93.09% over CAMELYON16 dataset. And the AUC over the cancer subtypes classification can be up to 96.03% and 98.82% over TCGA-NSCLC dataset and TCGA-RCC dataset, respectively. Implementation is available at: https://github.com/szc19990412/TransMIL.

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Zhuchen Shao".
Hao Bian".l, Yang Chen Yifeng Wang". Jian Zhang?. Xiangyang Ji Yongbing Zhang
Tsinghua Shenzhen International Graduate School, Tsinghua University
Harbin Institute Technology (Shenzhen)
Schooo of Electronic and Computer Engincering, Peking University *Department of Automation, Tsinghua University
Abstract
Multiple instance learning (MIL) is a powerful tool to solve the weakly super- vised classification in whole slide image (WSI) based pathology diagnosis. How- ever, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided proof for convergence. Based on this framework, devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL. effectively deal with unbal- anced/balanced and binary/multiple classification with great visualization and in- terpretability. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence com- pared with state-of-the-ar methods. The test AUC for the binary tumor classi- fication can be up to 93.09% over CAMELYON16 dataset. And the AUC over the cancer subtypes classification be up 96.03% and 98.82% over TCGA- NSCLC dataset and TCGA-RCC dataset, respectively. Implementation is avail- able at: https://gihhub.com/sz///// /ttp
Introduction
The advent of whole slide image (WSI) scanners, which convert the tissue on the biopsy slide into gigapixel image fully preserving the original tissue structure [1], provides a good opportunity for the application of deep learning in the field of digital pathology [2. 3, 4]. However, the deep learning based biopsy diagnosis WSI has to face a great challenges due to the huge size and the lack of pixel-level annotations(5). To address this problem, multiple instance learning (MIL) is usually adopted to take diagnosis analysis as a weakly supervised learning problem.
In deep learning based MII,, one straightforward idea is to perform pooling operation [6, 7] on instance feature embeddings extracted by CNN. Ilse et al. [8] proposed an attention based aggrega- tion operator, giving each instance additional contribution information through trainable attention weights. addition, et al. [9] introduced non-local attention into the MIL problem. By calculat- ing the similarity between the highest-score instance and the others, each instance is given different
"Contributed equally: 1 "Corresooddig author:
@ @gmail.com.
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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Positive Instance
Negative Instance
Uncertain Instance
h
h2
X3
22,1
d2
1/
h2
C2
h2
K2
22,2
1/2
hg
hg
Kg
0g.3
(a) MIL Attention Mechanism
(b) Self-attention Mechanism
Figure 1: Decision-making process. MII. Attention Mechanism: follow the i.i.d. assumption, Self-
attention Mechanism: under the correlated MII. framework.
attention weight and the interpretable attention map can be obtained accordingly. There were also other pioneering works [10, 11, 12. 13, 14] in weakly supervised WSI diagnosis.
However, all these methods are based on the assumption that all the instances in each bag are inde- pendent and identically distributed (i.i.d... While achieving some improvements in many tasks, this i..d. assumption was not entirely valid [15] in many cases. Actually, pathologists often consider both the contextual information around single area and the correlation information between differ- ent areas when making a diagnostic decision. Therefore, it would be much desirable to consider the correlation between different instances in MIL diagnosis.
At present, Transformer is widely used in many vision tasks [16, 17, 18, 19, 20, 21] due to the strong ability of describing correlation between different segments in sequence (tokens) well as modelling long distance information. As shown in Figure I, different from bypass attention network in the existing MIL, the Transformer adopts self-attention mechanism, which can pay attention to the pairwise correlation between each token within a sequence. However, traditional Transformer sequences are limited by their computational complexity and can only tackle shorter sequences (e.g.. less than 1000) [22]. Therefore, it is not suitable for large size images such as WSIs.
To address these challenges mentioned above, proposed correlated MIL framework, including the convergence proof and a generic three-step algorithm. addition, a Transformer based MII. (TransMIL) was devised to explore both morphological and spatial information between different instances. Great performance over various datasets demonstrate the validity of the proposed method.
2
Related Work
2.1 Application of MIL WSI classification
The application of MIL in WSIs be divided into categories. The first one is instance-level algorithms [7, 23, 24, 25, 26), where a CNN is first trained by assigning each instance pseudo-label based on the bag-level label, and then the top-k instances are selected for aggregation. However, this method requires a large number of WSIs, since only a small number of instances within each slide can actually participate in the training. The second category embedding-level algorithms, where each patch in the entire slide is mapped to fixed-length embedding, and then all feature embeddings are aggregated by an operator (e.g., max max-pooling). To improve the performance, the MIL attention based method [8. 10, 11, 12, 13] assigns the contribution of each instance by introducing trainable parameters. In addition, the feature clustering methods [14, 27, 28] calculated the cluster centroids of all the feature embeddings and then the representative feature embeddings was employed to make the final prediction. Recently, non-local attention [9] was also adopted MIL. to pay more attention to the correlation between the highest-score instance and all the remaining instances.

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2.2 Attention and Self-attention in Deep Learning
Attention was initially used to extract important information about sentences in machine translation [29]. Then the attention mechanism was gradually applied to computer vision tasks, including giving different weights feature channels [30] or spatial distribution [31], or giving different weights to time series in video analysis [32]. Recently, attention was also applied in MII. analysis [10, 11, 12, 13J. However, all these methods did not consider the correlation between different instances.
The most typical self-attention application was the Transformer based NLP framework proposed by Google [33]. Recently, Transformer was also applied in many computer vision tasks, including object detection [16, 17]. segmentation [18, 19], image enhancement [20, 21J and video processing [34]. In this paper, for the first time, we proposed a Transformer based WSI classification, where the correlations among different instances within the same bag are comprehensively considered.
3 Method
3.1 Correlated Multiple Instance Learning
Problem formulation Take binary MIL classification as an example, we want to predict target value Y; E (), given given hibit both dependency and ordering among each other. The instance-level labels {yr,1, V1.2, Vi.n unknown, and the bag-level label is Y,, for i I,...,b b. A binary MIL classification can be de- fined as:
bag of instances {æ,,1, 2.,...... Fi, with X,, for = 1,...,6 b, that ex-
0. iff Σ yis
Mi E {0.1), -1...
Y =
(1)
1, otherwise
Y(Xi))),
(2)
where S is a scoring function, Y; represents the prediction. b the total number of bags, is the number of instances in ith bag, and the number of can vary for different bags.
Compared to the MIL framework proposed by Ilse et al. [8], further introduce the correlation between different instances. Theorem and Inference give an arbitrary approximation form of the scoring function S(X), and Theorem 2 provides the advantage of correlated MII..
Theorem 1. Ve > O, for any invertible map P X + R" function and g, such that for any X E X:
Suppose S X -> R is a continuous set function w.r. Hausdorff distance d
SS(X) - g( P (o(x) x E X.))] <. xex
(3)
That is: a Hausdorff continuous function S(X) can be arbitrarily approximated by a function in the form g( P (a(x)) x E X.). XEX
Proof. By the continuity of S, we take Ye 0, de, that [S(X) ((XX) for any X,X X,
du (X, X') < de.
Define K = [ and define an auxiliary function: (xx) = |K K
Let = (a(x) : : E X). then:
SS(X= - 5(X) <E,
(4)
because dp (X, X) S 5e.
Let P: X - R be any invertible map, its inverse mapping is expressed as P-1; R. - X. Let
g S (P-1), then:
s p- (10(z) x x = (
p (x)) S(X).
(5)
XEX
XEX
Because [S(X) - S(X) E and s(X) = (P- = =P(P(X)
we have:
|S(X) - g( P (a(x) : E X <E
(6)
Xex
This completes the proof.
3

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13000
20000
mage
pooling
pooling
pooling
Self pooting
Figure 2: The difference between different Pooling Matrix P. Suppose there are 5 instances sampled from WSI in (a), P R is the corresponding Pooling Matrix, where the values in the diagonal line indicate the attention weight for itself and the rest indicate correlation between different in- stances. (b,c,d) all neglect the correlation information, hence the P diagonal matrix. (b), the first instance was chosen by Max- Max-pooling operator, so there is only one non-zero value in the first diagonal position.
(c), the values within diagonal line are the same due to the Mean-pooling operator. In (d), the values within diagonal line can be varied due to the introduction of bypass attention. (e) obeys the correlation assumption, so there are non-zero values off-diagonal position indicating correlation between different instances.
Inference Suppose S X - R is a continuous set function w.r. Hausdorff distance dss(s.),, )) VE > 0, for any function f and any invertible map P X + R", fuunctio h and g, such that for any X E X:
[S(X) o( P Px + h(z)
X <.
(7)
XEX
That is: a Hausdorff continuous function (XX can be arbitrarily approximated by a function in the form g( P h(x): a X)).
XEX
Proof. The proof is in the Appendix A.
Theorem 2. The Instances in the bag represented by random variables 11 O....., On. the information entropy of the bag under the correlation assumption can be expressed as H (01, (e....,,),, and the information entropy of the bag under the i..d (independeni and identical distribution) assumption can be expressed as Ei=1 H (9i), then we have:
(0 2...... θx) Σ H I H Σ (O.).
(8)
t=1
Proof. The proof is in the Appendix A.
Theorem proved the correlation assumption has smaller information entropy, which may reduce the uncertainty and bring more useful information for the MIL, problem. Motivated by the Inference and Theorem 2, generic three-step method like Algorithml was developed. The main difference between the proposed algorithm and existing methods is shown in Figure 2.
Algorithm 1: A generic three-step approach under the correlated MIL
Input: The bag of instances X, {(xr.) 1, Fi,2 ..., )in)
Output: Bag-level predicted label Y;
I) Extracting morphological and spatial information of all the instances by f and h, respectively;
X, + f(X,), X, < h(X,), Xyn < X, + X,,, where X,, X X fu E Ruxd,
2) Aggregating the extracted information for all instances by Pooling Matrix P:
Xp fh where P E Raxn
3) Transforming Xp to obtain the predicted bag-level label by gi
Y, r(Xr).

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Pretrained
Squaring
class token
Reature token
first M feature tokens
ResNetS0
Multi hend
PPEG
Multi-bead Self actenioon
Self self-imtettoon
Foad
cluss
Layer Neem
Layer Norm
Figure 3: Overview of our TransMIL. Each WST is cropped into patches (background discarded), and embedded in feature vectors by ResNet50. Then the sequence is processed with the TPT module: 1) Squaring of sequence; 2) Correlation modelling of the sequence; 3) Conditional position encoding and local information fusion; 4) Deep feature aggregation; 5) Mapping of + y.
3.2 How to apply Transformer correlated MIL
The Transformer uses self-attention mechanism to model the interactions between all tokens in a sequence, and the adding positional information further increases the use of sequential order information. Therefore it's 's good idea introduce the Transformer into the correlated MIL problem where the function h encodes the spatial information among instances, and the Pooling Matrix P uses self-attention for information aggregation. To make this clear, we further give formal denition.
Transformer based MIL. Given set of bags (x1.X2..... ..) and each bag X; contains multiple instances {Ir,1, .......... Fi,n and corresponding label Y, The goal to learn the mappings: X + T - V/ where X is the bag space, T is the Transformer space and y is the label space. The specific mapping form of T and T + available in the Appendix
3.3 TransMIL for Weakly Supervised WSI Classication
To better describe the mapping of X T, design TPT module with two Transformer layers and position encoding layer, where Transformer layers are designed for aggregating morphological information and Pyramid Position Encoding Generator (PPEG) is designed for encoding spatial information. The overview of proposed Transformer based MIL (TransMIL) is shown in Figure
Long Instances Sequence Modelling with TPT. The sequences are from the feature embeddings in each WSI. The processing steps of the TPT module are shown in Algorithm 2, where MSA de- notes Multi-head Self-atiention, MLP denotes Multilayer Perceptron, and LN denotes Layer Norm.
Algorithm 2:
TPT module processing flow
Input: A bag of feature embeddings H, (h. hi,n) where has R Blxd is the embedding of the jth instance, H, Raxd
Output: Bag-level predicted label Y:
I) Squaring of sequence;
VN +- [vn], M N Hg < Concat (Hi,class) H; (hi,i,..., h,, where h dlsss E 1 represents class token, Hş E (V++xxd
2) Correlation modelling of the sequence;
H + MSA (Hs), where denotes the layer index of the Transformer, HĄ E R(N+1)xd,
3) Conditional position encoding and local information fusion;
H + PPEG (H)), where H. E (N+1)xd
4) Deep feature aggregation;
H +1 < MSA (H.), where S (N++))xd
5) Mapping of T + ):
Y, MLP (LN (0)) where (H E Bixd represents class token.
5

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For most cases, the softmax used in Transformer for vision tasks such as [17, 18, 35] is a row-by- row softmax normalization function. The standard self-attention mechanism requires the calculation of similarity scores between each pair of tokens, resulting in both memory and time complexity of O(n²). To deal with the long instances sequence problem in WSIs, the softmax in TPT adopts the Nystrom Method proposed in [22]. The approximated self-attention form can be defined as:
QK? Vdgi
@K? Vdz
+
QK? Vag
softmax
softmax
softmax
where Q and K are the m selected landmarks from the original n dimensional sequence of and K, and A is Moore-Penrose pseudoinverse of A. The final computational complexity is reduced from On²)) to (n)). By doing this, the TPT module with approximation processing can satisfy the case where a bag contains thousands of tokens as input.
Position encoding with PPEG. In WSIs, the number of tokens in the corresponding sequence often varies due the inherently variable size of the slide and tissue area. In [36] is shown that the adding of zero padding can provide an absolute position information to convolution. Inspired by this, we designed the PPEG module accordingly. The overview is shown Figure 4, and the pseudo-code for the processing is shown in Algorithm 3.
Figure 4: Pyramid Position Encoding Generator. 1) The sequence is divided into patch tokens and class token; 2) Patch tokens reshaped into 2-D image space; 3) Different sized convolution kernels are used to encode spatial information; 4) Different spatial information are fused together; 5) Patch tokens are flattened into sequence; 6) Connect class token and patch tokens.
Our PPEG module has more advantages over the method proposed in [37]: (I) PPEG module uses different sized convolution kernels the same layer, which can encode the positional information with different granularity, enabling high adaptability of PPEG. (2) Taking advantage of CNN's abil- ity to aggregate context information, the tokens in the sequence able obtain both global infor- mation and context information, which enriches the features carried by each token.
Algorithm 3: PPEG processing flow
Input: A bag of feature embeddings H'S after correlation modelling, where H R(N+1)xd The feature embeddings H after conditional position encoding and local information fusion, where H E (N++)xx
Output:
1) Split: H is divided into patch tokens H, and class token He;
H,, He Split (H&). where H, Nxd H. R1xd,
2) Spatial Restore: patch tokens H are reshaped to H in the 2-D image space;
H Restore (H where H E RRVVx/NX
3) Group Convolution: using set of group convolutions with kernel k and k-1 zero paddings(k: = 3,5, 5, 7) to obtain H/ 1,2,3;
H.C - Conv (H)) where H. 1 W /xxdxXxxx- == 2,
4) Fusion: H and the H = 1, 2, obtained from the convolution block processing are added together to obtain H::
g H H + H. + H where H. RVNxVNXx,
5) Flatten: H are flattened into sequence H.c:
Hee +
Flatten (H where H., E Nxd;
6) Concat: connect Hee and class token H. to obtain H.:
H + Concat (H H.), where Hς IN++))xd

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Experiments and Results
To demonstrate the superior performance of the proposed TransMIL, various experiments were con- ducted over three public datasets: CAMELYONI6, The Caner Genome Atlas (TCGA) non-small cell lung cancer (NSCLC), as well as the TCGA renal cell carcinoma (RCC).
Dataset. CAMELYONI6 is a public dataset for metastasis detection in breast cancer, including 270 training sets and 130 test sets. After pre-pro pre-proessingg a total of about 3.5 million patches at x20 magnification, in average about 8,800 patches per bag were obtained.
TCGA-NSCLC includes two subtype projects, i.e., Lung Squamous Cell Carcinoma (TGCA-LUSC) and Lung Adenocarcinoma (TCGA-LUUAD), for a total of 993 diagnostic WSIs, including 507 LUAD slides from 444 cases and 486 LUSC slides from 452 cases. After pre-processing, the mean number of patches extracted per slide x20 magnification is 15371.
TCGA-RCC includes three subtype projects, i.e., Kidney Chromophobe Renal Cell Carcinoma (TGCA-KICH), Kidney Renal Clear Cell Carcinoma (TCGA- and Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP), for total of 884 diagnostic WSIs, including 111 KICH slides from 99 cases, 489 KIRC slides from 483 cases, and 284 KIRP slides from 264 cases. After pre- processing, the mean number of patches extracted per slide x20 magnification 14627.
Experiment Setup and Evaluation Metrics. Each WSI is cropped into a series of 256 x 256 non- overlapping patches, where the background region (5aturation<15)) is discarded. In CAMELYONI6 we trained on the official training set after splitting the 270 WSIs into approximately 90% training and 10% validation, and tested on the official test set. For TCGA datasets, different slides from one patient do not exist in both the training and test sets, and then randomly split the data in the ratio of training.validatiionntss 60:15:25. For the evaluation metrics, we used accuracy and area under the curve (AUC) scores to evaluate the classification performance, where the accuracy was calculated with threshold of 0.5 in all experiments. For the AUC, the official test set AUC was used on the CAMELYON16 dataset, the average AUC was used on the TCGA- NSCLC dataset, and the average one-versus-res AUC (veereea was used on the TCGA- RCC dataset. All the results over TCGA datasets are obtained by 4-fold cross-validation.
first ensured that
Implementation Details. In the training step, cross-entropy loss was adopted, and the Lookahead optimizer [38] was employed with a learning rate of 2e-4 and weight decay of 1e-5. The size of mini-batch B is 1. As in [13], the feature of each patch is embedded in 1024-dimensional vector by a ResNet50 [39] model pre-trained on ImageNet. During training, the dimension of each feature embedding is reduced from 1024 to 512 by fully connected layer. Finally, the feature embedding of each bag can be represented as H, E k2n25 In the inference step, the softmax is used to normalize the predicted scores for each class. All experiments done with RTX 3090.
Baseline. The baselines we chose include deep models with traditional pooling operators such as mean-pooling, max-pooling and the current state-of-the-art deep MIL models [8, 9, 13, 23, 40], the attention based pooling operator ABMIL [8] and PT-MTA [40], non-local attention based pooling op- erator DSMIL [9], single- attentioon-rannd CLAM-SB(13), nolli-attention-beee and recurrent neural network(RNN) based aggregation MIL-RNN [23].
CLAM-MB
4.1 Results on WSI classification
We will present the results of both binary and multiple classification. The binary classification tasks contain positive/negative classification over CAMELYONI6 and LUSC/LUAD subtypes classifica- tion over TCGA-NSCLC. The multiple classification refers to CICA--KCHHTCGAAKKIRCIC KIRP subtypes classification over TCGA-RCC. All the results are provided in Table 1.
In CAMELYON16, only a small portion of each positive slide contains tumours (averagely total cancer area per slide <10%), resulting in the presence of large number of negative regions disturb- ing the prediction of positive slide. The bypass attention-based methods and proposed MIL. all outperform the traditional pooling operators. However in AUC score, TransMII. was at least 5% higher than ABMIL, PT-MTA and CLAM which neglect the correlation between instances, and
7

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Table 1: Results on CAMELYONI6, TCGA-NSCLC and TCGA-RCC.
CAMELYONI6
TCGA-NSCLC
TCGA-RCC
Accuracy
AUC
Accuracy
AUC
Accuracy
AUC
Mean-pooling
0.6389
0.4647
0.7282
0.8401
0.9054
0.9786
Max- poooloo
0.8062
0.8569
0.8593
0.9463
0.9378
0.9879
ABMIL [8]
0.8682
0.8760
0.7719
0.8656
0.8934
0.9702
PT-MTA [40]
0.8217
0.8454
0.7379
0.8299
0,9059
0.9700
MIL-RNN [23]
0.8450
0.8880
0.8619
0.9107
\
DSMIL [9]
0.7985
0.8179
0.8058
0.8925
0.9294
0.9841
CLAM-SB [13]
0.8760
0.8809
0.8180
0.8818
0,8816
0,9723
CLAM-MB [13]
0.8372
0.8679
0.8422
0.9377
0.8966
0,9799
TransMIL
0.8837
0.9309
0.8835
0.9603
0.9466
0.9882
do not consider the spatial information between patches. DSMIL only considers the relationship between the highest scoring instance and others, leading to limited performance.
In TCGA-NSCLC, positive slides contain relatively large areas of tumour region (averagely total cancer area per slide >80%). consequently the pooling operator can achieve better performance than in CAMELYON16. Again, TransMIL performed better than all the other competing methods, achieving 1.40% higher in AUC and 2.16% in accuracy, compared with the second best method.
In TCGA-RCC, as MIL-RNN did not consider the multi-classification problem, it was not included in this comparison result. The TCGA-RCC is unbalanced distributed in cancer subtypes and has large areas of tumour region in the positive slides (averagely total cancer area per slide >80%). However, TransMIL is equally applicable to multi-class problems with unbalanced data. It can be observed that TransMIL achieves best results in both accuracy and AUC score.
4.2 Ablation Study
To further determine the contribution of the PPEG module and the conditional position encoding for the performance, have conducted series of ablation studies. Since the high classification accuracy of most methods over TCGA-RCC is not obvious, all ablation study experiments are based on the CAMELYON16 and the TCGA-NSCLC dataset. All experiments were evaluated by AUC.
4.2.1 Effects of PPEG
The position encoding of the Transformer typically explores absolute position encoding (c.g.. sinu- soidal encoding, learnable absolute encoding) as well as conditional position encoding. However, learnalle absolute encoding is commonly used in problems with fixed length sequences, and does not meet the requirement for variable length of input sequences WSI analysis, so is not taken into account in this paper. Here, we compared the effect of sinusoidal encoding and PPEG module which represents multi-level conditional position encoding. The same experiments are performed CAMELYONI6 and TCGA-NSCLE dataset, and the results are shown in Table 2. It should be noted that sinusoidal encoding is added to the original sequence with a multiplication of 0,001 as described in [33].
0.96
train
+1.33%
0.61%
Table 2: Effeets of PPEG.
0.95
train (order)
0.94
Model
Params
Camelyon16
NSCLC
+0.90%
-0.34%
w/o
2.625M
0.8416
0.9287
0.92
sin-cos
2.625M
0.8941
0.9374
3x3
2,630M
0,8913
0,9355
0.91
7x7
2.651M
0.9015
0.9336
both
2,669M
0.9059
0.9402
Camelyon16 test (w/o)
Camelyon16 test (order)
PPEG
2,669M
0.9309
0.9603
test (w/o)
test (order)
Figure 5: Effects of Positional Encoding.

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Compared with the model without position encoding, it can be seen that both sinusoidal encoding and conditional position encoding can improve the classification performance, and conditional po- sition information encoded by PPEG can be more effective in diagnosis analysis. In contrast to the 3 x 3 and x 7 convolutional block, adding different sized convolution kernels in the same layer allows for multi-level positional encoding and adds more context information to each token.
4.2.2 Effects of Conditional Position Encoding
Here, by disrupting the order of the input sequences, explore actual improvements for conditional position encoding. The performance of the model under different configurations shown Figure where order represents sequential data input and w/o represents random and disordered data input. can seen that conditional position information did enhance the model performance, e.g., the improvement can be up to 0.9% over CAMELYON16 and 0.61% % over TCGA-NSCLC in AUC.
Compared with the model without position encoding or with sinusoidal encoding, conditional posi- tion information encoded by PPEG can be more effective in diagnosis analysis. Compared with the results trained over the sequential and disordered training sets, conditional position information did enhance the model performance, e.g., the improvement can be to 0.9% over CAMELYON16 and 0.61% 1 over TCGA-NSCLC in AUC.
4.3 Interpretability and Attention Visualization
Here, will further show the interpretability of TransMIL. As shown in Figure 6(a). the within the blue curve annotation is the cancer region, which was provided by Gao et al. [41] over the TCGA-RCC dataset. In Figure 6(b), attention scores from TransMIL were visualised as heatmap to determine the ROI and interpret the important morphology used for diagnosis, and Figure 6(c) is zoomed-in view of the black square in Figure 6(b). Obviously, there a high consistency between fine annotation area and heatmap, illustrating great interpretability and attention visualization of the proposed TransMIL.
Tumour
0.5
200
(a) Annotated slide
(b) Attention heatmap
(c) Zoomed-in view of black square
Figure 6: Interpretability and visualization.
4.4 Fast Convergence
Traditional MIL. methods as well as the latest MII. methods such as ABMIL, DSMIL and CLAM usually require large number of epochs converge. Different from these methods, TransMIL makes use of the morphological and spatial information among instances, leading to approximately two three times fewer training epochs. As shown in Figure TransMIL has better performance in terms of convergence and validation AUC than other MIL methods.
5 Conclusion
In this paper, we have developed a novel correlated MIL framework that is consistent with the behavior of pathologists considering both the contextual information around a single area and the correlation between different areas when making diagnostic decision. Based this framework.

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10
605
Maeposing
Margeelg
ARMIL
656
DANIL
Vallaton MLC.
Velsauns ALC.
CLAM 58
CLAN MB
FTA-SITA
TreaNEE.
10
Fpscls
CAMELYONIG
(b) NSCLC
Figure 7: The convergence comparison of TransMIl. and the competing methods.
Transformer based MIL (TransMIL) was devised to explore both morphological and spatial informa- tion in weakly supervised WSI classification. We also design PPEG for position encoding well as a TPT module with two Transformer layers and position encoding layer. The TransMIl. network easy train, and can be applied to unbalanced/balanced and binary/multiple classification with great visualization and interpretability. Most importantly, TransMIL outperforms the state-of-the-arr MIL algorithms in terms of both AUC and accuracy over three public datasets. Currently, all the ex- periments were conducted over the dataset with x 20 magnification, however the WSIs with higher magnification will result in longer sequence and inevitably pose great challenges in terms of both computational and memory requirements, and we will explore this issue in the follow-up work.
Broader Impact Our proposed approach shows greater potential for MII. application real-workd diagnosis analysis, particularly in problems that require more correlated information such as survival analysis and cancer cell spread detection. In the short term, the benefit of this work is to provide model with better performance, faster convergence and clinical interpretability. In the long term, the proposed TransMIL network is more applicable real situations, and is hoped that it will provide more novel and effective ideas about further applications of deep MIL to diagnosis analysis.
Acknowledgment This work supported in part by the National Natural Science Founda- tion of China (619220988660331002))) part by the Shenzhen Science and Technology Project UCY12002010011220000844 and in part by Guangdong Special Support (2019TX05X187),
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12


Reviews:
[{"review_id": "kBZVZrvCYxy", "reviewer": "Reviewer_Bp25", "summary": "\tThe authors propose a Transformer-based MIL method for WSI classification.  \nThe method is designed to avoid the IID assumption inherent to standard MIL inference methods (e.g. mean, max pooling). Secondly, they propose a positional encoding module for patches in WSIs based on CNNs to incorporate spatial information into the prediction, along with semantic information learned in the MSA layers. In the ablation study, they demonstrated that PPEG model performed better than conventional sin-cos positional encoding. They used three WSI classification datasets from open sources with previously benchmarked results. The authors were able to achieve SOTA results on each task. \n", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Originality: original work\n\nQuality: very well done, figures, tables, algorithms look excellent\n\nClarity: The paper is dense and the authors attempted to cover a lot of ground. The writing is clear for the space alotted. \n\nSignificance: Paper is significant for moving WSI classification forward by applying Tranformers to MIL.  \n\nStrengths:\nThis paper is the natural extension of previous RNN-based and attention-based MIL methods for WSI classification in computational pathology. The application of Transformers to patch-based WSI classification is a natural one given the recent success of Transformers for NLP (patches-as-token). \nThe authors selected three representative WSI datasets for model testing.\nFigure 2 is excellent and provides a clear illustration of the difference between the MIL inference methods using an explicit pooling matrix. \nThe authors are commended for stating explicitly, and addressing, that an IID assumption for patch-based WSI classification is incorrect and should be avoided in the development of models moving forward.   \n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Weakness:\nOne major problem, or point of misunderstanding, is how the ‘Spatial Restore’ step in Algorithm 3 PPEG processing flow is related to the actual spatial relationships between patch locations in the WSI. The input order of patches into the MSA module is unclear from the paper, (random, sliding window, etc. ?), but there is no ‘natural’ square in the WSI. Yet in processing the patch tokens (H_f), an artificial 2-d square feature map is generated. With the patch tokens now populating the ‘pixels’ in the feature maps, adjacent patch tokens/pixels are not necessarily locally connected and ought not to belong in the same convolutional receptive field. I assume this is what the authors mean by ‘context information', however, it is unclear how the PPEG module provides the appropriate patch-level spatial context within the WSI. Different sized convolutions do not address this problem. \n\nIf the above criticism is correct, does the model perform differently when patch tokens are randomly shuffled versus structured sliding-window patching (semi-ordered)? If they do not, then PPEG is learning to perform some other function than incorporating spatial/context information. \n\nAdding attention maps from CLAM versus ABMIL versus TransMIL to Figure 6 would be constructive. As proposed, it is unclear what is actually driving the boost in performance: better spatial/bypass attention or correlative/self-attention. \n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3 "}, "extra_text": {}}, {"review_id": "HunsE6IfMFU", "reviewer": "Reviewer_XM4a", "summary": "The paper presents a transformer based approach for WSI classification, correlation among instances are considered to address the iid assumed by previous methods. The methods were tested on three public datasets, i.e. Camelyon16, TCGA-NSCLC and TCGA-RCC datasets, and the results suggest that the performance for binary and multiple category classification is better than state of the art. ", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "\nThe idea of using transformr to process those instances for WSI classification seems to be an interesing idea and a good application for transformer. The authors shall include the previous work \"Patch Transformer for Multi-tagging Whole Slide Histopathology Images\" and discuss the main differences.  The experiments shall also include this work for comparison, as this is the closest work.\n\nFor WSI images, while image-level classfication is useful, it is also important to mark those cancer cells such that the classification can be justified. For a WSI (bag) classified as carconoma, losts of instances (patches)  included in the bag might be normal cells. How do these cells interfere with the classification results?  Can the proposed approach (transformer, correlated MIL) mitigate such issues? More detailed analysis on this problem are expected.\n\nMinor problems:\n1.\tThere are some spelling errors through the manuscript, e.g. the title “Classication” should be “Classification”. Please check the manuscript carefully.\n2.\tFor the related work, the author only listed some literatures, but without giving a discussion about the strength and weakness of the existing works.\n3.\tIn Table 1 we can see that, besides the CAMELYON16 dataset, the baseline MIL-based methods showed much lower performances than max-pooling. Please give some discussions about the reason.\n4.\tFor ablation study, The Table 2 and Fig. 5 were not mentioned in the manuscript. What do the values stand for in Table 2 and Fig. 5? Why giving the detailed discussion in Appendix? I suggest move the discussion to the main manuscript.\n5.\tFor Fig.6, What is the purpose to show the zoom-in view of heatmap? I cannot see anything special in this area.\n6.\tFor Fig. 7, the initial accuracy of MIL-based baseline model were higher than the converged models, especially for the NSCLC dataset, why?\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "n/a", "review_text": "", "extra_scores": {"ethical_concerns": "n/a", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "9ClZRFNnAI6", "reviewer": "Reviewer_GWgS", "summary": "The authors propose a transformer based model for the classification of whole slide histopathology images. The method first extract ResNet features locally across WSIs, then apply a transformer based model on top of the features. The authors also propose a Pyramid Position Encoding Generator (PPEG) module for transforming local features and encode positional information. The proposed framework is general and can be applied to other non-medical image analysis tasks.\n\nThe proposed method achieves better result compared to recent baselines.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The theorems give some analysis on why multi-head self-attention layers may be good for this application. However, conclusion of \"smaller information entropy\" is not exciting. To some extent, it is almost straightforward to think that adding cross-patch correlation is beneficial. The paper is somewhat hard to follow. The authors introduced terms like TPT and PPEG before these terms were explained.\n\nI have a couple of concerns on experiments:\n1. The authors claim that the multi-head self-attention layer models the correlation across patches. To me, the convolutional layers in the PPEG module also mix features across patches, and thus, uses spatial patterns (correlation) across patches. It is not clear to me if simply stacking multiple PPEG modules would perform similarly, compared to the proposed approach. It would be great if authors can show the effectiveness of the attention layers via ablation studies.\n2. The authors compared their proposed PPEG against commonly used positional encoding methods such as sin-cos. It is not clear to me if the comparison is fair. First, without any non-linearity layer between self-attention layers, the self-attention layers tend to converge to a trivial state of all tokens having the same feature vector. Thus, if the authors use sin-cos as the baseline method, the authors should explain if they still put some layers with non-linearity in the place of PPEG. Second, PPEG introduces computation and increases model capacity, where as sin-cos barely does. It is not clear if the performance gain is due to increased model capacity, or due to the intended mechanism.\n\nMinor problems:\n1. What does \"TPT\" stand for?\n2. \"discraded\" should be \"discarded\" in the caption of Figure 3.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors can show failure cases and discuss possible improvements.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "1.5"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper has an interesting application (if rather straightforward) of transformers on Multiple Instance Learning for classification of Whole slide images. The authors successfully responded to the reviewer criticisms. All reviewers recommend acceptance. 

Authors are encouraged to include the additional experiments and explanations to the final version
Decision: Accept (Poster)