A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis
Authors: Xiaoyu Cui
Weixing Chen
Jiandong Su
Venue: ICLR 2025 Poster (2025)
Keywords: Causal Inference
Pathological Image Analysis
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multiple Instance Learning (MIL) in digital pathology Whole Slide Image (WSI) analysis has shown significant progress. However, due to data bias and unobservable confounders, this paradigm still faces challenges in terms of performance and interpretability. Existing MIL methods might identify patches that do not have true diagnostic significance, leading to false correlations, and experience difficulties in integrating multi-scale features and handling unobservable confounders. To address these issues, we propose a new Multi-Scale Frequency Domain Causal framework (MFC). This framework employs an adaptive memory module to estimate the overall data distribution through multi-scale frequency-domain information during training and simulates causal interventions based on this distribution to mitigate confounders in pathological diagnosis tasks. The framework integrates the Multi-scale Spatial Representation Module (MSRM), Frequency Domain Structure Representation Module (FSRM), and Causal Memory Intervention Module (CMIM) to enhance the model's performance and interpretability. Furthermore, the plug-and-play nature of this framework allows it to be broadly applied across various models. Experimental results on Camelyon16 and TCGA-NSCLC dataset show that, compared to previous work, our method has significantly improved accuracy and generalization ability, providing a new theoretical perspective for medical image analysis and potentially advancing the field further. The code will be released at https://github.com/WissingChen/MFC-MIL.

Published as a conference paper at ICLR 2025
A MULTISCALE FREQUENCY DOMAIN CAUSAL FRAMEWORK FOR ENHANCED PATHOLOGICAL ANAL-
YSIS
Xiaoyu Cui Weixing Chen Jiandong Su
Northeastern University Sun Yat-sen University *Shenzhen Institute of Advanced Technology ou miiiimmiie neu. edu. en, chenwx228 @eeewx228@maille @mail2 12. sysu. edu. en, j jiandong. laurence. su@gmail.... com
ABSTRACT
Multiple Instance Learning (MIL) in digital pathology Whole Slide Image (WSI) analysis has shown significant progress. However, due to data bias and unobserv- able confounders. this paradigm still faces challenges in terms of performance and interpretability. Existing MIL methods might identify patches that do not have true diagnostic significance, leading to false correlations, and experience difficul- ties in integrating multi-scale features and handling unobservable confounders. To address these issues, we propose a new Multi-Scale Frequency Domain Causal framework (MFC). This framework employs an adaptive memory module to es- timate the overall data distribution through multi-scale trequency-domain infor- mation during training and simulates causal interventions based on this distribu- tion to mitigate confounders in pathological diagnosís tasks. The framework in- tegrates the Multi-scale Spatial Representation Module (MSRM), Frequency Do- main Structure Representation Module (FSRM), and Causal Memory Interven- tion Module (CMIM) to enhance the model's performance and interpretability. Furthermore, the plug-and-play nature of this framework allows it to be broadly applied across various models. Experimental results on Camelyon16 and TCGA- NSCLC dataset show that, compared to previous work, method has signifi- cantly improved accuracy and generalization ability, providing a new theoretical perspective for medical image analysis and potentially advancing the field fur- ther. The code will be released at https: /ttp//ggtttt//tt com/ wi/w/issssa http//iissnnghee//// MFC MIL
INTRODUCTION
The classification of Whole Slide Images (WSIs) involves the use of automated techniques to tract critical features from pathological slides and perform classification, thereby aiding in disease diagnosis Litjens al. (2017). This technology has the potential to enhance diagnostic efficiency and accuracy while reducing human error, which is particularly crucial for the early detection of cancer |aadbbuushi & Lee (2016). However, this task is challenging due to the high resolution of the images, which can contain billions of pixels, with diagnostically relevant regions often compris- ing only small portion. This complexity complicates the process of feature identification Komura & Ishikawal (2018). Furthermore, variations in staining techniques and other sources of noise the images can introduce model bias and lead to misjudgment Srinidhi et al. 2202 Li et al. (0024).
To address these challenges, multiple instance learning (MIL) methods have been developed for classification of WSIs. MIL methods reduce the computational requirements of WSI by select- ing multiple diagnostically relevant patches to represent the entire image. demonstrated in extensive fusks|lse et al. (2018); Shao et al. () Yao et al. (2019) 2020): Xu et al. (2019). Moreover, strate- gies for selecting these relevant patches help minimize the impact of noise, thereby improving the model's 's effectiveness IZheng et al. (2024). However, the inclusion redundant, irrelevant informa- tion can introduce data biases, potentially causing these methods mistakenly associate non-causal
"Equal Contribution.

---

Published as a conference paper at ICLR 2025
features with diagnostic outcomes as confounders, leading to erroneous conclusions Schwab et al. (2019); Chen et al. (202)).
Spurious correlations are caused by confounders that, while co-occurrig with disease states in the data, do not have a direct causal relationship with disease outcomes Pearl & Mackenzic 2018); Liu et al. (2022b). As illustrated in Figure [ (a), if positive and negative samples in the training set are predominantly associated with specific colors, the model may incorrectly associate color with pathological categories, leading to spurious correlations. Consequently, when positive samples in the test set share the same color as negative samples, the model may misclassify them due to these previously established spurious correlations. The Structural Causal Model (SCM) analysis Pearl et al. (2016) suggests that this eπrrr occurs because the model fails to correctly follow the causal link
instead relying on an incorrect causal path, X < - Y, as shown in Figure (b).
To eliminate these spurious correlations, IBMIL tions by estimating and removing confounders Z to reduce bias, as shown in Figure (c). However, the two-step training strategy used in IBMIL increases both the complexity and computational cost of the methods. Similarly, CaMIL Chen et al. (202)) adopt the front-door intervention for decon- founding with the estimation of mediator M as shown in Figure (d). However, CaMIL relies on preprocessed features to represent the overall distribution of the dataset and requires cone-connuming feature clustering processes. Moreover, through in-depth analysis of pathological diagnosis, we find that tissue structure at low magnification and cellular structure at high magnification is critical for accurate diagnosis (Schmitz et al. (202T). Existing methods generally handle features from sin- gle magnification or rely on preprocessed
in et al. (202)) utilize causal back-door interven-
features, which not only increase computational complexity but also hinder the comprehensive capture of spatial relationships across multiple levels Li et al.] (202T). Additionally, current structural information extraction methods are often vulnerable interference from image staining techniques and color contrast, leading to mis- classifications Vahadane et al. (2016); Tellez et al. 2019)).
To address these challenges, we propose the Multi- Frequency Domain Causal (MFC) frame- work, which consists of three key components: the Causal Memory Intervention Module (CMIM), the Multiscale Spatial Representation Module (MSRM), and the Frequency-domain Structural Rep- resentation Module (FSRM), as illustrated in Figure bias by preventing the model from relying on spurious correlations for decision-making By pre- serving critical diagnostic features as learnable memory features, CMIM facilitates plug-and-play causal interventions, eliminating unobservable confounders' misleading effects. The MSRM ad- dresses the challenge of integrating multilevel information by combining positional encoding with multiscale large-kernel convolutions, enabling the model to capture the spatial relationships hetween low-magnification tissue structures and ligh-aagnification cellular structures, thereby enhancing its ability to represent multilevel features. Finally, the FSRM integrates phase information in the frequency domain reduce interference from staining techniques and color contrast, extracting structural information that is directly related to diagnostic outcomes. Together, these three modules enable the MFC framework to perform pathology image classification tasks with greater accuracy and robustness.
The CMIM is designed to mitigate data
2 RELATED WORK
2.1 MULTIPLE INSTANCE LEARNING
In the task of WSI classification, Multiple Instance Learning (MIL) methods have become the pre- dominant approach for handling high-resolution pathology images by segmenting them into multiple patches and aggregating these patches the bag level to achieve efficient classification Ilse et al. (2188;; Li et al. (202T): Shao et al. (202T): Zhhng et al. (022). These methods utilize various aggregation strategies and attention mechanisms to enhance the model's ability to capture criti- cal information, thereby improving classification performance. However, they still face limitations in accurately selecting instances and recognizing complex pathological features, particularly when dealing with heterogeneous data, which can lead to information loss misclassification. To address these issues, researchers have proposed several enhancements. such as optimizing instance selection to improve the efficiency of utilizing critical patches Lu et al. (2021): Zheng et al. (2024). applying stain normalization techniques to reduce color variation between samples Tellez et al. (2019), and enhancing model adaptability across different datasets through domain generalization Stacke et al.
2

---

Published as a conference paper at ICLR 2025
(8)
Positive
Negative
Positive
(b)
(c)
X: Instance feature
Y: prediction
2: confounder
M: mediator
(e)
Feature Extraction
P(X)
P(M)X)
EP(YMM,))((S
MFC
FSRM
CMIM
Solbnee
Predict
FSRM
CMIM
Figure (a) WSI sample. (b-d) Causal diagrams for no intervention, back-door intervention, and front-door intervention. (e) We propose Multi-Scale Frequency Domain Causal Multi- Instanen Learning (MFC-MIL), framework that plug-and-play compatible with various MIL models. In the MIL model, WSI is typically encoded as CLS tokens and feature tokens (patch tokens). Our MFC framework extends this approach by applying the MSRM and FSRM modules specifically to the feature tokens. The output from these modules is then fed into the memory module of the CMIM for interaction, ultimately influencing the CLS token.
2020). While these improvements often increase robustness and generalization, they also tend to add computational complexity and still fall short of effectively integrating multilevel pathological information Li et al. (202T). In contrast, our proposed approach introduces multilevel spatial repre- sentation and causal intervention mechanisms, which not only simplify the model architecture but also effectively address challenges arising from spurious correlations and complex feature integra- tion, significantly improving both accuracy and generalization capabilities.
2.2 CAUSAL INFERENCE
In medical image analysis, causal intervention methods aim to explicitly model and intervene in causal relationships to reduce the model's reliance on spurious correlations, thereby improving di- agnostic accuracy Castro et al. (2020); Liu et al. (2023). These approaches typically involve identi- fying and adjusting for potential confounders Nie al. (2023): Lin et al.) (2022) or mediators Chen et al. (2023) to correct spurious correlations caused by bias during inference, leading to more accu- rate capture of causal relationships relevant to disease outcomes. WSI classification. IBMIL Lin et al. (2023) and CaMIL (hen et al. (2024) are two prominent causal intervention strategies. IBMIL employs a back-door intervention to climinate the influence of confounders on classification results, but its two-stage training process is complex and requires the prior identification and maintenance of a confounder set, increasing implementation difficulty. CaMIL, by contrast, uses a front-door intervention strategy remove unobservable confounders via mediators. However, its reliance on clustering methods to select mediators during training increases computational time and may re- duce the interpretability of the mediators. In contrast, our approach combines multilevel spatial representation with requency-domaad structural representation as mediators, simplifying the train- ing process by avoiding cumbersome feature clustering and complex confounder management. This not only enhances computational efficiency but also improves the model's causal interpretability and classification accuracy.
METHOD
In this section, we first introduce the Causal Memory Intervention Module (CMIM), followed by discussion of two key representation modules: the Multiscale Spatial Representation Module (MSRM) and the Frequency-domait Structural Representation Module (FSRM). We then explain how MSRM and FSRM are integrated into the MIL framework and how CMIM is utilized for causal intervention.
3.1 CAUSAL MEMORY INTERVENTION MODULE
MIL models for WSI classification tasked with detecting the presence of positive instances within a bag containing numerous instances. at least one positive instance is present, the bag is classified positive; otherwise, it is classified negative. We assume that S
3

---

Published as a conference paper at ICLR 2025
(a) Causal Memory Intervention Module
(b) Multisccae Spatial Representation Module
Linear & softmax
matmul
(PEGG
[CLS]
Convad (3x3)
Hivntry
TCLSTi
Xat
Paachl
Conv (5x55
[Panal]
[iinaar softmer
matorul
Conv 2d (7x7)
Xat
Linear
matmul
offeee&& & inear
MaxPoollng
Convid
Coavld
Convid
GELU
Xn
Linear
concat
Linear
Linear
Figure 2: The illustration of our proposed modules in MFC-MIL, including the Causal Memory Intervention Module and Multiscale Spatial Representation Module.
(P1,3) (P2, ... (Pn: yn)) represents a WSI sample S containing n patches with correspond- ing instance labels y. The bag label Y can then be formulated as follows:
0,
iff E
9/4 =
-0
1, otherwise
Since the instance-level labels are usually unavailable, we must estimate the bag-level label based on the predicted instance-level labels. Specifically, in the MIL framework, the process involves using frozen visual encoder f()) to map patch-level images into low-dimensional feature representations. which constitutes the feature extraction step. An aggregation module 0-(-) is then employed to combine these instance-level features into a bag-level representation, followed by a classification module (-)-) to estimate the bag-level label Y, as formulated as:
Y (c(00((1,, ... x.), f(p)).
However, due to the presence of confounders, the non-causal model does not follow the correct causal path X - Y for prediction. Instead, it relies on spurious correlations established by the confounders, following the incorrect path X t
which can be formulated as:
- - P
where, X { T2, 3 represents the patch-level features and Z represents the confounders that lead to spurious correlations, which are typically difficult to estimate, especially in the absence semantic extractor. Therefore, the features generated by the aggregator 6,
of well-traine treated as the mediators, and the do-operator do(.) is introduced to apply causal front-door inter- vention for deconfounding, effectively cutting off the link X Z Y. The total probability P(Y do())) can be expressed as the following summation:
P(Y Σ P(Y)do(X), m) mldo(X)),
where, M is introduced by X without any back-door path, and there is no direct causal relationship between X and Y. Additionally, the link M + X + 2 + Y can be further severed to achieve deconfounding. Finally, the Eq.44
14.4caa can be rewritten as follows:
P(M=))
M
(5)
m
where å represents the potential estimated values selected from x. The detailed derivation can be found the Appendix A..I] However, previous methods typically use clustering to estimate f, which significantly hinders computational efficiency. Therefore, we propose utilizing memory module to estimate the overall distribution of the dataset during training and to refine the estimation of & through attention- based sampling. Specifically, set of trainable parameters with length & initialized as memory and combined with attention-weighted inputs to select relevant memory elements. This selected memory is then further sampled and used as á in the front-door intervention,
4

---

Published as a conference paper at ICLR 2025
as illustrated in Figure (a), (more detail can be found in Appendix A.1). Finally, we employ the Normalized Weighted Geometric Mean (NWGM) Liu et al. (2022a) to estimate the equation.
Specifically, to implement this equation effectively, we need to address two challenges: (1) estimat- ing the overall distribution P(X = ă) without explicit clustering, and (2) ensuring that the mediator M captures causal structural information while being robust against confounders. We solve the first challenge through our memory module, which learns to represent the distribution of features across the dataset. For the second challenge, we employ specialized rrequencydooaald and multi- scale representations that focus on structural patterns rather than color or staining characteristics that often serve as confounders in pathological images. This approach allows us implement the front-door adjustment in an end-to-end trainable framework without requiring explicit identification of confounders or mediators.
3.2 MULTISCALE SPATIAL REPRESENTATION MODULE
After implementing CMIM, we further refined the estimation of the mediator, particularly by in- tegrating low-magnification tissue information with high magniaiaiiiiini cellular information, which both erucial in the diagnostic process. We propose the Multiscale Spatial Representation Mod- ule (MSRM), which first applies Position-aware Patch Embedding Generation (PPEG) [Shao et al.] (202I) for multiscale positional encoding, followed by sampling with multiple large-kernel convo- lutions, as illustrated in Figure (b).
Specifically, X E R(N,D) is padded using the sampled patches in the PPEG and reshaped to X E RID. : 2,,, where N represents the number of patches in the current slide, D the model dimension, and N₂ is the number of patches after padding. After passing through 2D convolutional layers with kernel sizes of 7, 3 and 5, the padding is removed, and the original dimensions are re- stored, resulting in high-resolution features X R(N.D) Then, three 1D) convolutional layers, each with a kernel size of 16, are applied with dilation rates of I, 3 and to extract features with multiscale receptive fields. This process can be formalized as follows:
Xu Linear (ConarCoovv1))) + Convy (envz((An)) (Xm) + Convy(Xni) + MaxPooling(Xxw) (6)
Here, Xu E D) represents the low-magnification information obtained through sampling, while the MaxPooling layer is used residual connection to provide additional information about tissue contours. Additionally, the joint dimension between the convolutional layer and linear is D,
3.3 FREQUENCY-DOMAII STRUCTURAL REPRESENTATION MODULE
The Hilbert transform is tool utilized to derive the analytic representation of a real-valued signal. Applying the Hilbert transform to signal rff), we obtain complex-valued function x.((), where the original signal forms the real part, and the Hilbert transform provides the imaginary part. The analytic signal xa (t) can be expressed as:
(a(()) = sx()) + j$(()
where (t) the Hilbert transform of xtt) given by:
x(() dr
(() P.V.
(8)
P.V. denotes the Cauchy principal value of the integral.
In this complex representation, the real part axt)) retains the original amplitude information of the signal, representing its observable component. The imaginary part @(t), on the other hand, provides the quadrature component, which is essential to understand the phase characteristics of the signal. Importantly, the imaginary part (t) I) is the phase-shifted version 90 degrees of the real part x(t). By combining the real and imaginary parts, we can obtain a comprehensive representation that captures significantly richer information than spatial domain features alone.
The Frequency-doman domain Structural Representation Module (FSRM) addresses the need to extract nu- anced and informative features from Whole Slide Images (WSIs) enhance classification accuracy.
5

---

Published as a conference paper at ICLR 2025
Traditional spatial-domain methods often fail to capture subtle pathological indicators, risking the loss of critical diagnostic information. Using frequency domain analysis, the FSRM reveals hidden patterns and relationships within image data that may not be evident in the spatial representation. This capability is particularly advantageous WSI analysis, where complex tissue structures and cellular arrangements exhibit distinctive frequency signatures.
The FSRM consists of several key components working in concert. First, an input projection layer maps initial features to an appropriate dimensional space, preparing them for frequency-domain transformation. At the module's core, the Hilbert transform is implemented through Fast Fourier Transform (FFT), which first calculates the FFT of the signal, then constructs frequency domain filter (doubling the positive frequency components), and finally obtains the real part through inverse FFT. The transformed features are subsequently mapped back to the original dimensional space via an output projection layer. To maintain original feature information and support gradient flow during training, the entire module integrates residual connection.
The overall transformation function F R + R is defined as:
F(x) = x + ((H
where: f : R2 + Rs12 is the input linear mapping f(x) = Wix + b and g R2 + Rd the output linear mapping g(x) Wax + by. H the Hilbert transform operator. The final output can be expressed as: design allows the FSRM to enhance feature representation by capturing complex structural and textural variations critical for robust WSI classification. It is worth emphasizing that the FSRM and CMIM modules play complementary roles within our framework, While the Hilbert Transform focuses extracting sesquuncc-domailnn structural information, endowing the representation with robustness against superficial artifacts such as staining bias, the CMIM performs causal intervention to eliminate confounding factors within the data.
+ ((H((x))) = x + W2(H(WIx + b1)) + b2. This
4 EXPERIMENT
4.1 DATASET AND METRIC
The Camelyon 6 dataset Bejnordi et al. (2017) is widely used for detecting breast cancer metastases. It comprises
270 training and 129 testing images, segmented into about 3.2 million patches of 256 256 pixels at 20x magnification, averaging 8,000 patches per slide. Meanwhile, the TCGA-NSCLC dataset focuses two lung cancer subtypes, LUSC and LUAD, with 1,054 whole slide images. It divided into training, validation, and test sets in 7:1:2 ratio, yielding 5.2 million patches at 20x magnification, with about 5,000 patches per slide.
To evaluate the effectiveness of our approach, we apply four key metrics for classification perfor- mance: accuracy, F1 score, specificity, and the area under the receiver operating characteristic curve (AUC). These metrics provide a comprehensive assessment of the method's 's overall performance.
4.2 IMPLEMENTATION SETTINGS
In the feature extraction process, we employed CNN-based ResNer18, with parameters pre-trained using SimCLR as part of the DSMIL framework. The model operates with dimension of 512, while the value of CMIM is set 16 for high-resolution features and 32 for low-resolution features. For most experiments. we used the Adam optimizer with initial learning rate of 2e-4 and weight decay of 5e-4. Additionally, our MFC estimates the mediator using patch-level features and applies it to intervene in the aggregated bag-level prediction vector. The mini-batch size used for training is 1, and the model is trained for 100 epochs. All experiments were conducted on an NVIDIA GeForce RTX 2080TT.
4.3 BASELINE
ABMIL Ise et al. (2018) enhances multi-instanee learning through attention mechanisms, focus- ing on critical image regions to improve pathological classification performance. DSMIL Li et al. (202T) employs a dual-stream network structure with self-supervised contrastive learning to en- hance the accuracy of whole-slide image classification. TransMIL. Shao et al. (2021) utilizes

---

Published as a conference paper at ICLR 2025
transformer- bassed approach for relevant multi instance learning, aiming to better capture key infor- mation within images. CLAM Chen et al. (2024) leverages glustering-constraiee
attention- based multiple instance learning to enable efficient, interpretable, and adaptable slide-level pathology clas- sification without manual annotations. DTFD-MII. /Zhang et al. (2022) leverages a dual-layer fea- ture distillation strategy for multi-instance learning, optimizing the performance of tissue pathology classification for whole-slide images.
4.4 EXPERIMENT RESULT
Table I presents the results of our WSI classification experiments on two benchmark datasets, Came- lyon and TCGA-NSCLC, using 5-fold cross-validation. Overall, all MIL methods showed sig- nificant improvement after applying the MFC framework, demonstrating the effectiveness of our approach. Specifically, the DSMIL method, which models multiscale features, achieved an aver- age accuracy gain of 5.27% on Camelyon16 and 2.08% on TCGA-NSCLC. This indicates that the proposed MFC framework can more effectively leverage multiscale information. Morcover, we ob- served that the performance improvements those on the TCGA-NSCI.C dataset, consistent with the findings from IBMIL Lin et al. (2022). This can be attributed to the more severe data bias in the 1amelyon16 dataset, where positive bags contain only a small fraction of positive instances (approximately less than 10%). This difference further highlights the ability of MFC effectively identify multiscale structural information (the mediators), that is relevant to diagnosis and to intervene in bag-level predictions.
the Camelyon16 dataset were generally greater than
Additionally, it can be observed that our MFC framework significantly improves accuracy and the Fl metric, while the enhancement in the AUC metric is relatively minor. Notably, in methods such as CLAM-SB and CLAM-MB on the Camelyon16 dataset, the performance worse compared to the baseline in terms of AUC, despite substantial improvements in other metrics. This suggests that MFC alters the sample distribution in the data, such that the model better handles certain boundary samples, leading to increases in Fl and specificity. However, the handling of non-boundary samples is less balanced than before, which could negatively impact the overall AUC performance. This effect is particularly pronounced in high- nigh-dimessionl,, complex data such as pathological image classification, where the treatment of boundary and misclassified samples may significantly affect specific metries. Since AUC provides more comprehensive evaluation, it is likely more sensitive to these subtle changes, especially when the baseline model already performs well on this metric.
Furthermore, reproduced the IBMIL model and conducted five-fold cross-validation using DSMIL as the baseline on the Camelyon16 dataset. As shown in the table, our method consistently outperforms IBMIL, even though IBMIL also surpasses the baseline's performance. However, as previously mentioned, while MFC effectively handles certain boundary samples and further im- proves accuracy and the Fl metric, it also results in a suboptimal performance in the AUC metric.
4.5 ABLATION STUDIES
To further evaluate the effectiveness of our method, we conducted detailed and comprehensive ab- lation experiments, including the removal of individual modules and tests on key parameter settings for each module.
4.5.1 EFFECTIVENESS OF CMIM
As shown in Table 3 the CMIM model significantly outperforms the baseline, particularly exhibiting an improvement nearly 10% in the specificity metric. This suggests that CMIM is more effective in capturing causal features, especially in distinguishing negative samples. However, the enhanced performance on negative samples shifts the decision boundary, making the identification of positive samples more conservative, which in turn leads to decrease in recall.
To further investigate the impact of the memory mechanism in CMIM, we conducted an experi- mental analysis of the number of memory slots, denoted as k within the MFC-MIL framework. Specifically, we input cellular structural features (œ)л) and tissue structural features (zz) into the memory module, utilizing the activated memory for front-door intervention. In these experiments, we fixed the memory slot count for 2n at 32 and varied the memory slots for Iy at 4, 8, 16. 32, and 48. As shown in Figure 3 (a), the model's performance improved steadily when k ranged from
7

---

Published as a conference paper at ICLR 2025
Camelyon16 AUC
TCGA-NSCLC
Mothod
ACC
F1
Spe.
Method MFC
ACC
AUC
FJ
Spe.
ARMIL
88,8494
95.65(1.85)
84,40(4.16
93.75(7.81)
91.38/188) 99,10(0.06)
90.72(2.3)
98.00(1.
MFC
91.78(1877)
97 97.68(0.29)
88,94(207)
95.0044,76)
92.23(1.03)
(6)
91.70(1.24)
98.46(0.44
+2.94(3.11)
+2.03(187)
+4.54(4.65)
+5,25(911)
+0.85(2.14)
+0.14(0.27)
+0.98(2.61)
+0.46(1.29)
DSMIL
76.98(4.73)
94.554443)
98.002288) 194119.11)
DSMIL
(9.61(.72)
96.75(0.13)
(8.61(2.02
98 (8.461
+ MFC
92.25(2.13)
15.41(1.12)
89.13(3.42)
91.25(1.63)
+ MFC
91 9169(1.04)
98.95(0.13)
9..2001.31)
98.08(1.84)
+5.27(6.27)
-0.46(4.57)
19.72(9.73)
-0,5((3311)
+2.08(2011)
+2.2(0.18)
0.38(1 (90241)
TrunsMIL
84,50(2.74)
94.88(1.73)
80.9001.555 55)
63,500 10.25)
TransMlL.
91.54(2.39)
90.93(287) (8.54(
97.38(2.01)
+ MFC
67.68(()07)) 90.85(1.18)
88.00 88.00(1.13)
92. 72.75()
+ MFC
98.98(0.122 92.85(0.97)
9..30(1.13)
(3.53))
+6.35(2.88)
+2 83)2.03)
17.10)))94
19
+1.310309)
(0,54(0.55
+0,15(2.13)
CLAM-SB
86.69(7.09)
46.891444
84.75(16.04) 84,3015.51)
CLAM-SB
90.85(1 60)
99.05(0.22)
10.12
97.85(0.84)
+ MPC
89.77(1933)
96.11(1.24)
85.87(2110)
38.999
+ MFC
91.31(0.8)
99.22(0.16)
90.67(1.U7)
98.900.69)
+2.01(7.35)
0.69(190))
(3.75(16.3)) +1,5715.90)
+0.46(183)
10.17(0.2)
+0,15(1.09) +0.55(2.22)
CLAM-MB
88.9912.65)
97.65(0.32)
KS. 18(3.88)
82.0199.911
CLAM-MBS + A
91.22(2.91)
99.01(0.08)
(0.5((3.66
57,
+ MFC A
97.29X0.52) (1.55(
44(0.55)
98.50)
91.85(142)
19.10(0.11)
1.94))
46.46(4335
+2.95(2.71)
-0.36(0.61)
+3,26(3.89)
+16.5(10.04)
+0.63(3.24)
(0.09(0.14)
+0.55(4.04)
+0.47(4.38)
DTED DTFD (MaxS)
97 59(0007)
95.01K4.05) (39.9()))
DTFD (MaxS)
(1.77
58.520)))44
80.3200588)
86. 36.31(2)95)
MFC A
97.65(0.22) 92.09(1.77)
88 62(2.89)
98.50X2.71)
MFC
91.23(1.66)
90.54/2.06) 98,88(0.07)
98.15(0.88
+6.2(13.52) +0.06600.2) +17.21(40.04)
+3.5(4.87)
A
+9.92(182) +10.36(0.08)
+10.22(2.14)
+11.84(3.08)
Table I: Main result (%) on Camelyon and TCGA-NSCLC, the value in parentheses is the stan- dard deviation of the 5-fold vross-validation. However, the values in parentheses in the A column represent the propagated uncertainties, denoted as σ = V(o + <B²), where A corresponds to the base model and B refers to the model enhanced using MFC.
Method
ACC
AUC
Pre.
Rec.
Spe.
Baseline
86.98
94.95
14
68.98
79.41
98.00
IBMIL
91.78
96.31
94.85
83.67
88.50
96.75
MFC-MIL
92.25
94.96
84.08
89.13
Table 2: Comparison of the results (%) of our MFC-MIL and IBMIL on the Camelyon16 dataset, and the baseline is DSMIL.
CMIM
MSRM
FSRM
ACC
AUC
Pre.
Rec.
F1
Spe.
84.50
94.88
78.01
86.12
80.90
83.50
88.37
97.53
89.92
81.63
83,78
92,50
89.46
97.61
91.61
81.63
85.45
94.25
97.68
89.25
87.76
88.00
92.75
Table 3: Ablation result (%) of MFC-MIL on Camelyon16 dataset, and the baseline model is Trans- MIL.
16, suggesting that increasing the number of memory slots enhances the model's ability to capture cell-level pathological features. However, when k increased to 32 or 48, performance de- clined, implying that an excessive number of memory slots may introduce redundant information. In high-dimensional feature spaces. this could result in capturing noise irrelevant features, leading to overfitting and reduced generalization capacity.
Furthermore, in experiments where zns was fixed at 16 memory slots while varying the memory slot count for zu (Figure 3 (b)), a similar trend was observed. Both ACC and AUC improved as increased from 4 to 16 but declined when l reached 48. Interestingly, the F1 and specificity metrics followed an opposite pattern. This may be due to zn representing global tissue-level structural information, which emphasizes macroscopic pathological structures. At lower magnifications, the model better distinguishes normal tissues, resulting in a significant increase in specificity. However, as the reliance on global information grows, the model's sensitivity to subtle cell-level pathological features diminishes, leading to more false negatives. This explains why, at higher k values, F1 scores decrease while specificity remains high, approaching 100%.
4.5.2 EFFECTIVENESS OF MSRM
As shown in Table 3 the MSRM module effectively captures information across different seales, from local details to global structures, by processing features at multiple scales. This multi-scale feature integration significantly improves the model's performance, particularly in terms of speci- ficity and precision. The high specificity indicates the model's enhanced ability to identify negative
8

---

Published as a conference paper at ICLR 2025
Baveline i k-d i 1-88 D1-16
in kk-32
i k--45
Baselinc #k-d i ml-l mk-16
mi-332 i k--48
100
95
95
90
50
80
Spc.
Spe.
(b)
Figure 3: Ablation studies of the memory number & of ænd (shown as (a)) and za (shown as (b)) for
CMIM on 1a 6 dataset, and the baseline is TlansMII..
Joint Dimension of MSRM
Frequency Information Extraction of FSRM
size of D
ACC
AUC
F1
Spe.
Method
ACC
AUC
Fi
Spc.
Baseline
84.50
94,88
80.90
83.50
Baseline
84.50
94.88
80.90
83.50
512
90.85
97.68
88.00
92.75
HFT
88.37
91.66
83.52
95.00
1024
89.92
97.88
87.62
87.5
92.19
2048
87.6
97.3
80.49
100
DWT
89.15
97.93
83,72
98.75
4096
84.5
91.73
82.46
77.5
Hilbert transform
90.85
97.68
88.00
92.75
4: Ablation result of MSRM and FSRM on Camelyon16 dataset, and the baseline is TransMIL.
samples. However, the unchanged Rec. suggests that while the model improves in recognizing negative samples, it does not yield further gains in detecting positive samples.
Additionally, as indicated in Table 4 experiments conducted with various Dj settings demonstrate that the model achieves optimal performance ACC and FI metrics when D, is set to 512. This suggests that the original feature dimensionality is sufficient to capture relevant information, and further expansion is unnecessary. Although increasing the dimensionality to 1024, 2048, and 4096 theoretically enhances the model's expressive capacity, in practice, it leads to performance degra- dation. This decline may result from the model capturing excessive noise irrelevant information, which negatively impacts generalization. Particularly at D 4096, the model's performance be- comes comparable to the baseline, indicating that excessive feature dimensionality expansion may completely offset the potential benefits of multi-scale feature extraction.
4.3.3 EFFECTIVENESS OF FSRM
In the FSRM, applying the Hilbert transform to both high-magnification and low-magnification fea- tures allows the model to capture richer structural information. In WSIs, frequency domain data reveals intricate details that are often missed in the traditional spatial domain. As shown in Table the introduction of FSRM improved key metries such as ACC, AUC, recall, and F1, demonstrating that this module significantly enhances the model's generalization ability and sensitivity to lesion areas. However, despite FSRM's strong performance in capturing structural features, there was a de- cline in precision and specificity compared using only CMIM and MSRM. Specifically, precision dropped from 91.61 % to 89.25 89.25%, and specificity decreased from 94.25% to 92.75%. This suggests that while FSRM improves the model's sensitivity positive samples, may reduce its ability to distinguish negative ones.
Analysis highlights the superiority of the Hilbert transform over the Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), particularly in AUC and F1 scores. The Hilbert transform's is ability capture instantaneous phase information crucial for processing complex pathological images. In contrast, FFT and DCT focus on magnitude varia- tions, often missing phase characteristics essential for detecting subtle changes. For example, FFT achieved an AUC of 91.66666 91.66% far below the 97.68% of the Hilbert transform, showing FFT's global averaging fails to represent local structural details effectively. DCT, while achieving high speci- ficity (96.25%) by emphasizing fow-frequency information, underperformed in recognizing positive samples. Despite this, DCT lagged behind the Hilbert transform in Fl and AUC, demonstrating the Hilbert transform better balances global and local features. Compared to DWT, which offers multi- resolution analysis but is limited by fixed basis functions, the Hilbert transform more effectively captures rapid intensity variations and local irregularities like cell membranes. Its robustness in

---

Published as a conference paper at ICLR 2025
tracting diagnostic features while resisting staining biases ensures higher precision in tasks requiring detailed structural analysis.
CONCLUSION
This study introduces MFC-MIL, novel and flexible framework that addresses critical challenges in pathological image classification by leveraging multiscale spatial and frequency domain informa- tion. Through its three key modules-MSII MSRM, FSRM, and CMIM MMMCMMI not only enhances diagnostic accuracy but also demonstrates deep understanding of the inherent complexities in WSIs. The model's ability to preserve spatial correlations across magnifications, mitigate color con- trast variations, and reduce confounding factors reflects a sophisticated approach to medical image analysis. Future work could explore incorporating regularization methods based on Rényi entropy, suggested in recent studies, to enhance feature representation and memory capacity Guan et al. (2024); Wang et al. (2024). Experimental results on the Camelyon16 and TCGA-NSCLC datasets highlight significant improvements in accuracy, F1 score, and specificity, underscoring the frame- work's ability to more precisely distinguish between positive and negative samples. This study offers important insights into the trade-off's between recall and specificity, revealing how CMIM's causal interventions- while reducing false positives may introduce a more conservative decision bound- ary. Such enhancements might improve model stability under confounding factors, making causal intervention more robust and effective. Nonetheless, the overall performance gains, particularly in handling complex, high-dimensional data, suggest that MFC-MIL's integration of causal reasoning and multiscale representations sets new standard for WSI analysis. These findings not only ad- vance the current state of medical image classification but also open new avenues for research into more interpretable and reliable diagnostic tools in clinical practice.
ACKNOWLEDGEMENTS
This work is supported by the National High-tech Research and Development Program (Grant No. 20022222FC2508000) Liaoning Provincial Natural Science Foundation (Grant No.2022-MS-105) and the Medical and Engineering joint fund of Liaoning Province (Grant No.2022-YGJC-76)
REFERENCES
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases women with breast cancer. Jama, 318022):119-22210 2017.
Daniel C Castro, Ian Walker, and Ben Glocker. Causality matters in medical imaging. Nature Communications, 11(1):3673, 2020.
Kaitao Chen, Shiliang Sun, and Jing Zhao. Camil: Causal multiple instance learning for whole slide image classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 1120-1122 128, 2024.
Weixing Chen, Yang Liu, Ce Wang, Jiarui Zhu, Guanbin Li, Cheng-Lin Liu, and Liang Lin. Cross- modal causal intervention for medical report generation. arXiv preprint a7Xiv:2303.09117, 2023.
Xianchao Guan, Yifeng Wang. Yiyang Lin, Xi Li, and Yongbing Zhang. Unsupervised momti-domain progressive stain transfer guided by style encoding dictionary. IEEE Transactions on Image Pro- cessing, 2024.
Maximilian Ilse, Jakub Tomezak, and Max Welling. Attention- based deep multiple instance learn- ing. In International conference on machine learning, pp. 2127-2136. PMLR, 2018.
Daisuke Komura and Shumpei Ishikawa. Machine learning methods for histopathological image analysis. Computational and structural biotechnology journal, 16:34-42, 2018.
10

---

Published as a conference paper at ICLR 2025
Bin Li, Yin Li, and Kevin W Elicciri. Dual-stream multiple instance learning network for whole slide image classification with self-super contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14318-14328, 2021.
Zilong Li, Qi Gao, Yaping Wu, Chuang Niu, Junping Zhang, Meiyun Wang. Ge Wang, and Hong- ming Shan. Quad-net: Quad-domain network for ct metal artifact reduction. IEEE Transactions on Medical Imaging, 43(5): 1 866-1879, 2024.
Tiancheng Lin, Hongteng Xu, Canqian Yang, and Yi Xu. Interventional multi-instance learning with deconfounded instance-level prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp. 1601-1608 2022.
Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, and Chang-Wen Chen. Interventional bag multi- instance learning on whole-slide pathological images. In Proceedings of the IEEE/CVF Confer- on Computer Vision and Pattern Recognition, pp. 19830-19839, 2023.
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. survey on deep learning in medical image analysis. Medical image analysis, 42: 60-88, 2017.
Bing Liu, Dong Wang, Xu Yang, Yong Zhou, Rui Yao, Zhiwen Shao, and Jiaqi Zhao. Show, de- confound and tell: Image captioning with causal inference. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 18041-18050, 2022a.
Yang Liu, Yu-Shen Wei, Hong Yan, Guan-Bin Li, and Liang Lin. Causal reasoning meets visual representation learning: A prospective study. Machine Intelligence Research, pp. 1-27, 2022b.
Yang Liu, Weixing Chen, Guanbin Li, and Liang Lin, Causalvir: A toolbox and benchmark for visual-linguistic causal reasoning. arXiv preprint arXiv:2306. 17462, 2023.
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. Dfta-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering, 5(6):555-570, 2021.
Anant Madabhushi and George Lee. Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical image analysis, 33: 33:170-175, 2016.
Weizhi Nie, Chen Zhang, Dan Song, Yunpeng Bai, Keliang Xie, and An-An Liu. Chest x-ray image classification: A causal perspective. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 25-35. Springer, 2023.
Judea Pearl and Dana Mackenzie. The Book Of Why: The New Science Of Cause And Effect. Basic books, 2018
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. Causal Inference In Statistics: Primer. John Wiley & Sons, 2016.
Rüdiger Schmitz, Frederic Madesta, Maximilian Nielsen, Jenny Krause, Stefan Steurer, René Werner, and Thomas Rösch. Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations the global tissue architecture. Medical image analysis, 70:101996, 2021.
Patrick Schwab, Djordje Miladinovic, and Walter Karlen. Granger-causal attentive mixtures of ex- perts: Learning important features with neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 4846-4853, 2019.
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang. Xiangyang Ji, al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Ad- vances neural information processing systems, 34:2136-211 2021.
Chetan L Srinidhi, Ozan Ciga, and Anne L Martel. Deep neural network models for computational histopathology: A survey. Medical image analysis, 67:101813, 2021.
11

---

Published as a conference paper at ICLR 2025
Karin Stacke, Gabriel Eilertsen, Jonas Unger, and Claes Lundström. Measuring domain shift for deep learning in histopathology. IEEE journal of biomedical and health informatics, 25(2):325- 336, 2020.
David Tellez. Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen Van Der Laak. Quantifying the effects of data augmentation and stain color normal- ization in convolutional neural networks for computational pathology. Medical image analysis, 58:101544, 2019.
Abhishek Vahadane, Tingying Peng. Amit Sethi, Shadi Albarqouni, Lichao Wang, Maximilian Baust, Katja Steiger, Anna Mclissa Schlitter, Irene Esposito, and Nassir Navab. Structure- preserving color normalization and sparse stain separation for histological images. IEEE trans- actions on medical imaging, 35(8):1962 1971, 2016.
Yifeng Wang, Jiangtao Xu, and Yi Zhao. Wavelet encoding network for inertial signal enhancement via feature supervision. IEEE Transactions on Industrial Informatics, 2024.
Gang Xu, Zhigang Song, Zhuo Sun, Calvin Ku, Zhe Yang. Cancheng Liu, Shuhao Wang. Jianpeng Ma, and Wei Xu. Camel: A weakly supervised learning framework for histopathology image segmentation. In Proceedings of the IEEE/CVF International Conference computer vision, pp. 10682-10691, 2019.
Jiawen Yao, Xinliang Zhu, and Junzhou Huang. Deep multi-instance learning for survival predic- tion from whole slide images. In Medical Image Computing and Computer Assisted Intervention- MICCAI 2019: 22nd International Conference, Shenzhen, China, October 1 13-17, 2019, Proceed- ings, Part 1 22, pp. 496-504. Springer, 2019.
Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, and Junzhou Huang. Whole slide images based cancer survival prediction using attention guided deep multiple instance learn- ing networks. Medical Image Analysis, 65:101789, 2020.
Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng. Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathol- ogy whole slide image classification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18802-18811 2022.
Tingting Zheng, Kui Jiang, and Hongxun Yao. Dynamic policy-driven adaptive multi-instance learn- ing for whole slide image classification. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 8028-8037, 2024.
A APPENDIX
A.1 THE DERIVATION OF EQ 5
The derivation from Eq. equation to Eq. equation 5 involves several key steps grounded in the principles of causal inference. These steps are detailed as follows:
Step 1: Introduction of the Mediator M. In Eq. equation P(Y P [do(X)) is expressed by
marginalizing over the mediator M, M fully mediates the causal effect of X on Y. and there exists no back-door path between X and M. Specifically, the equation is written as:
P(Y(do(X)) = Σ P(Y]do(X), M P(M = mldo(X)).
Since M is introduced directly by X without confounding, the intervention do(X) does not alter the conditional distribution of M given X, i.e. P(M]do(X)) P(M]X). This simplifies the expression to:
P(Y)do(X)) = Σ P(YY)do(X),M MMM =m = x).
P(M
122
12

---

Published as a conference paper at ICLR 2025
Since M is introduced by X, need to explain why there is no confounding between X and M in our framework. The mediator M is derived from the frequency domain and multi-scale spatial representations of the imput features X, which specifically capture structural information rather than color or staining biases (which are common confounders in pathological images). By operating in the frequency domain through the Hilbert transform and across multiple scales through MSRM, mediator extracts diagnostic-relevan structural patterns that are largely invariant to the confounders Z affecting X. This transformation from the spatial domain frequency and scale-space domains creates a new representation pathway that is more robust against the confounding factors that typ- ically affect raw image features, such as staining variations, color biases, and imaging artifacts. Empirical evidence for this can be seen in our ablation studies in Section 4.5.3, where using fre- quency domain features consistently outperforms spatial domain features, supporting our claim that M is less affected by the same confounders as X.
Step 2: Eliminating do( from do M m). further simplify, we note that P(Y do(X),
m) can be replaced with P(Y M m), where å represents poten- tial values of X. This substitution is valid under the assumption that M fully mediates the effect of X on Y, and hence, the causal effect of X on Y through M is independent of the intervention X)) Substituting this into the equation yields:
P(Y|do(X)) E P(M P(M=mlp
m|X P =Z, M m).
Step 3: Incorporation of Potential Values of X. The introduction of P(X .) accounts for the possible values that X can take, ensuring that the causal effect is evaluated over the distribution of X. This step is critical for severing potential confounding pathways, such as M X Z - ensuring that the deconfounding is achieved as outlined in the front-door criterion.
Step 4: Final Expression Combining the above steps, the final expression for P(Y]do(X)) is obtained as:
P(Y P(Y]do(X))
Σ P(M== m|X = x) P(X-=2)((1) 2, M m). m
This form explicitly integrates the mediator M and the potential values à, enabling a precise repre- sentation of the causal effect P(Y)do(X)) in accordance with the front-door adjustment.
Normalized Weighted Geometric Mean (NWGM) To estimate P(Y)d)(X)) using the Normal- ized Weighted Geometric Mean (NWGM), integrate its ability balance contributions from multiple conditional probabilities while maintaining robustness to noise and outliers. NWGM is defined as
NWGM .2,...,, Inj W1, 1/2
W.,)
Σ we
where x; are input values and wy their corresponding weights. In the front-door adjustment, NWGM can be applied to combine P(Y = &. .M m) and P(X .)) with weights reflecting their relative importance, ensuring both are proportionally integrated. This leads to the reformulated estimation:
P(Y(do(X)) = Σ P(M m| = =) NWGGM(P = 2, = m) = =));
W1, w); W2).
By leveraging the logarithmic transformation during computation, NWGM maintains numerical sta- bility while preserving the geometric properties of the integrated probabilities, ensuring robust and accurate causal effect estimation.
A.2
MORE DETAIL MFC
A.2.1 MSRM.
In this task, MIL is typically divided into three steps: feature extraction, feature aggregation, and classification, with most improvements focusing on the feature aggregation step. In our framework, the input for each training inference iteration consists of all patch features X from given slide.
13

---

Published as a conference paper at ICLR 2025
Specifically, after applying PPEG, the padded feature tensor E TT is reshaped back to X R(,,.)) and the previously added padding is removed, resulting in Xui R(N,D), Next, 1D convolution with stride of 16 is applied along the dimension N to scale the features, producing Xu E R P).
A.2.2 CMIM.
Regarding the CMIM, we initialized a set of k trainable memory vectors hm E Rexd within the CMIM module, where is the dimension of the model. The input X is processed through linear layer Ow to obtain a set of memory write weights Wws which interact with to activate memory him based on the current input formulated as:
hm hm ww X, Wwy s0ftmax
(10)
where is the dot operation. Subsequently, X estimates set of memory read weights w,, and selects from the activated memory to facilitate the estimation of X in the causal intervention process as formulated following:
X + Wrr hi,, Wi (Xf))
(11)
where 6, is linear layer whose weights are not shared with 6...
Taking TAI as an example, in this framework, Ihz serves as the mediator variable M in the front-door adjustment formula. The output X generated by the memory module (i.e., X = X + hmm) is used to decompose the direct causal effect of X on Y. Specifically, the memory module activates the memory h. during the writing phase (ww X ), modeling P(M) X). In the reading phase, X is generated by combining w,, hm with X, modeling P(Y X). Through the causal chain X - M + X + Y, the memory module effectively implements front-door adjustment. By leveraging the attention mechanism, it transfers the influence of X to X, breaking the direct pathway between X and Y. thereby controlling confounding effects and enhancing causal modeling capability.
It's important to clarify the role of Thi in our causal framework. While The contributes to the com- putation of the mediator M, it is not directly equivalent to M. Rather, M is derived from the spe- cialized representation of zis through the FSRM and MSRM modules, which transform the original features into frequency-domate and multi-scale structural information. This transformation creates a new representation that captures diagnostic-releve relevvat information while being less susceptible to confounders.
In our implementation, the front-door criterion is preserved because the pathway from X to Y intercepted by this transformed representation (M). The expression X = X + wr * ht should be understood combining the original information with the memory mediated information, where the memory component (W,, hm)) is influenced by M but not directly by X. This formulation allows us to balance the retention original feature information while incorporating the causally-adjusted representation, rather than completely replacing X with M, which would potentially lose valuable information.
In practice, this means the model learns to down-weight the direct influence of X when spurious correlations are detected and ue-weight the memory meditty pathway, effectively implementing the front-door adjustment a soft, learned manner rather than hard intervention.
The attention mechanism in our memory module implements causal intervention through several key mechanisms:
Distribution Modeling: During training, the memory module (hm)) learns store represen- tations of the overall data distribution across multiple slides, providing a reference against which individual slide features can be compared. This is conceptually similar to estimating P(X) in the front-door formula.
The write weights (ww) activate specific memory elements based on input features, modeling the conditional probability P(M X). This activation is content- based rather than position- based, enabling the model to identify similar structural patterns across different slides.
Selective Activation:
14

---

Published as a conference paper at ICLR 2025
Intervention Mechanism: The read weights (w,) select from the activated memory gener- ate the adjusted representation X, which serves as the intervention component in the front- door adjustment. This process models the term Σ; P(X P = 2, M = m) in Equation 5.
Causal Flow Control:
By adding the memory-mediated information (w,, lim)) to the origi- nal features X, the model learns to control the flow of information between the direct path (potentially containing spurious correlations) and the memory-mediated path (containing more reliable diagnostic patterns).
Through extensive experimentation (see Section 4.5.1), we found that this formulation effectively mitigates the influence of confounders, as evidenced by the significant improvements in specificity and overall accuracy. The ablation studies in Table 3 further support this claim, showing that remov- ing CMIM leads to substantial performance degradation, particularly in specificity, which is most sensitive to spurious correlations.
A.2.3 FSRM.
The Hilbert transform H[x(()) itself maps real-valued signal to another real-valued signal. The an- alytic signal z(t) is then constructed by combining the original signal xt)) with its Hilbert transform as:
=(1) = ax1)) + i+[((())
(12)
where i is the imaginary unit, this analytic signal 2tt) is indeed complex-valuee but this is different from the Hilbert transform itself. The text should be revised avoid this confusion and clearly distinguish between:
The Hilbert transform: H[x(t)) (real-valued to real-valued)
The analytic signal: x(t) xxt) + [[)))tt) (complex- -omplex-valued)
In histopathological images, the presence of cell membranes and tissue boundaries represents struc- tural discontinuities in biological tissues. These anatomical features are characterized by sharp tran- sitions in pixel intensities, manifesting as local discontinuities from signal processing perspective. The instantaneous phase information obtained through the Hilbert transform demonstrates superior capability in capturing these intricate morphological details, particularly regions of rapid intensity variations.
The overall transformation function F: Rd R2d is defined as:
F(x) = x + ((H(((())))))
(13)
where: f: Rd -> R512 is the input linear mapping fxx) W,x + bj and g R512 - Rd is the output linear mapping g(x) W2x + b2 H is the Hilbert transform operator. The final output can be expressed as: y = + g (f())) = + (2(H(WW + b,)) + b2 Additionally, FSRM operates in the feature space, ensuring that the input and output dimensions remain consistent. For instance, X E R(N.D) and X E Rl D) retain their respective shapes after processing.
A.3 THE RATIONALE FOR EXCLUDING ORIGINAL IMAGE FEATURES FROM THE CMIM PIPELINE.
In pathological diagnosis tasks, differences in staining techniques can influence the model due to color variations in pathological images. During model training, spurious correlations may be es- tablished between diagnostic results and image colors, leading to incorrect predictions. However, pathological diagnosis primarily focuses on cell morphology and tissue structure rather than color. Therefore, we aim to structural features rooted in eiqquency-domatte information inputs in- stead of original image features affected by staining bias.
To further validate this perspective, compared two additional model variants. As shown in Ta- 5le MFC-a incorporates original image features into the inputs of the CMIM module. In contrast, MFC-3 introduces an additional CMIM module that takes original image features as input and ad- justs the [CLS] token through a separate causal intervention module. The final output obtained by summing the probabilities from both modules.
15

---

Published as a conference paper at ICLR 2025
Mehhod
ACC
AUC
PT
Spe.
ABMIL
88.8412.40 49)
95.65(1.38)
84.40(4.T6)
93.
91.78(187)
97.68(0.29)
88.94(2.07)
MIC-a
86.05(7.67)
97.69(0.09)
84.1166.75)
91.25(5.91) 87.5(9.26)
85.27(6.17)
17(.54)55)
83.47(7.13)
Table 5: An ablation study is conducted on the inputs to the CMIM, the value in parentheses is the standard deviation of the 5-fold cross-vale voss-validation.
Camlyon
Method
AUC
Spe.
ARMIL
81.86 (3.49)
84.12 12.
70.04 (9.24)
96.75 (2.44)
+
(0.55)
85.54 (3.58)
77.28 (1.35)
93.50 (5.41)
+2.64
+1.42
+7.24
-3,25
TransMIL
81.71 (1.78)
78.57(4)46)
68.99(4.06)
98. 78.75(1)3))
(0.69)
83.82(2.40)
76.88(2.03)
96.50(2.40
+3.25
+5.25
+7.89
-2.25
TCGA NSCLC
Method
Spe.
ABMIL
86.69(1.60)
96. 18((6))))2)
1.752)
87.6919.91)
+ MPC
87.23(1.00)
96.40(0.41)
86, 1..3))
93.08(6.23)
+0.54
+0.22
-0.10
+5.39
TransMIL
88. 88.85(L110)
96. 98(0.69)
88.04( 1.59)
95.38(2.18)
39.42(1.31)
96.94(0.47)
88.95(1.61)
93.46(3.17)
+0.57
-0.04
+0,91
-1.92
Table 6: Main result (%) on Camelyon16 and TCGA-NSCLC dataset, which CTransPath extracts the features.
Method
ACC
AUC
F1
ABMIL
88.84(2.49)
95.65(1.85)
84.4014.16)
93.75(7.88 11)
91.08(2.01)
97.71(0.56)
88.01(2.08)
94.79(3.31)
+ MRC
91.78(1887)
97.68(0.29)
88.94(2.07)
95.00)4.76)
DSMIL
86.98(4.73)
94.95(4.43)
79.41(9.11)
08.00(2.88)
IRMIL
91.78(2.30)
96.31(0.56)
88.50(3.50)
96.75(4.64)
+ MFC
92.25(2.33)
95.41(1.12)
89.13(3.42)
97.25(1.63)
TransMLL
84.50(2.74)
94.88(1.73)
80.90(158)
83. 10.255
IBMIL
90.80(1.12)
96. 19(0.83)
86.46(1.10)
90.09(5.67)
+ MFC
90.85(1.18)
(.06)
18.00(1.133
92.75(5.77)
Table 7: Comparison of the results (%) of our MFC-MIL and IBMIL on the Camelyon16 dataset.
We conducted experiments on the Camelyon 16 dataset using ABMIL as baselines. The results show that although MFC-a achieves exceptional performance in terms of AUC, it underperforms com- pared to the baseline models and MFC on other metrics, with its standard deviation also increasing further. Meanwhile, the performance of MFC-8 is even worse than MFC-a, which further sup- ports our viewpoint. However, MFC-3 incurs a larger parameter count than MFC, as it includes an additional CMIM module with inputs from original image features at high and low magnifications.
A.4 THE FEATURE EXTRACTION AND THE USE OF BACKBONES.
We followed the preprocessing method of DSMIL, using the same features as employed in most pathology-related MIL studies. Specifically, DSMIL utilizes ResNet18, which has been pre-trained on pathological data using SimCLR.
To further validate the effectiveness of our method, we also conducted experiments using the features adopted by IBMIL. Specifically, used CTransPath the feature extraction model and evaluated our method against the ABMIL and TransMIL baselines on the Camelyon16 and TCGA-NSCLC datasets, demonstrating its effectiveness, as shown in Table Our MFC framework delivers superior model performance compared to the baselines, along with more reliable results as evidenced by consistently lower standard deviations.
A.5 MORE COMPARISONS WITH IBMIL
We compared the performance of MFC and IBMIL across three models (e.g., ABMIL, DSMIL, and TransMIL), using 5-fold cross-validation on the Camelyon16 dataset, as shown in Table The results show that end-to-end training framework, MFC, achieves better overall performance
16

---

Published as a conference paper at ICLR 2025
compared the two-stage training approach used by IBMIL. However, in DSMIL and ABMIL, our method slightly underperforms IBMIL in terms of AUC. Additionally, IBMIL exhibits smaller performance standard deviations than MFC.
This difference may stem from IBMIL's two-stage training, which provides stronger directional guidance for confounders during the first stage. In contrast, our end-to-end approach, while effective, might introduce overfitting in the memory module, leading to increased variability.
17


Reviews:
[{"review_id": "xTQ5noYwGn", "reviewer": "Reviewer_p1Pm", "summary": "The paper proposes a multi-scale frequency domain causal framework for WSI classification that addresses the challenges of data bias and unobservable confounders. The framework comprises three key components: 1) a multi-layer spatial representation module, 2) a frequency domain structure representation module, and 3) a causal memory intervention module. The authors present comprehensive experimental results to validate their approach.", "questions": "1.\tWhat is the rationale for excluding original image features from the CMIM pipeline?\n2.\tPlease provide detailed specifications of the modules:\n      a.\tMemory elements\n      b.\tAttention mechanism implementation\n      c.\tMemory element selection criteria\n      d.\tWeighting methodology\n3.\tPlease clarify the technical implementation details:\n      a.\tHilbert transform application to feature vectors\n      b.\tProjection layer architecture\n      c.\tScope and implementation of residual connections (FSRM module vs. complete MFC)", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThe extensive experimental validation effectively demonstrates the performance of both individual components and the complete system\n2.\tThe novel application of frequency domain analysis to mitigate irrelevant factors such as color variations represents an innovative approach\n3.\tThe overall paper structure and flow are well-organized, though some core concepts would benefit from more precise articulation", "weaknesses": "1.\tThere are significant inconsistencies between the abstract and main content. The abstract references concepts such as information bottleneck and backbone fine-tuning that are absent from the paper. While the general writing is clear, the technical descriptions of the proposed modules lack sufficient precision and detail\n2.\tThe mathematical formulation of the novel components is inadequate. While the paper includes basic equations from prior work, the core innovative concepts lack rigorous description. Specific areas requiring mathematical formalization include: 1) Lines 200-207: Memory module operations and 2) Lines 262-269: Feature transformation procedures\n3.\tThe causal inference framework appears to be limited to Hilbert-transformed images, excluding original image data. This design choice requires either theoretical justification or empirical validation\n4.\tSeveral claims made in the conclusion lack supporting evidence in the main text, including: 1) Trade-offs between recall and specificity, and 2) CMIM's capability to reduce false positives\n5.\tMinor Issues: a. Inconsistent capitalization (e.g., \"pathology\" in abstract, \"FDSR\" in line 254) b. Strange visualization in Figure 2(b) c. Absence of proper introduction for technical abbreviations (FFT, DCT)", "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": "", "extra_scores": {}}, {"review_id": "3hKvr1AYJG", "reviewer": "Reviewer_7Fj5", "summary": "The authors stated that the existing DL method learns from a single magnification of pathological images, which limits the model’s accuracy. The authors proposed a new model named MFC, which includes the CMIM, MSRM, and FSRM modules. CMIM is designed to address the issue of spurious correlations by preserving diagnostic features as learnable memory elements and facilitating causal interventions. It reduces the influence of confounders, ensuring that the model makes decisions based on causal relationships rather than coincidental patterns in the data. This module helps improve model robustness by using attention-weighted sampling to refine the estimation of memory elements that contribute to more accurate predictions. MSRM integrates information across multiple scales to capture spatial relationships between different levels of image detail, such as tissue structures at low magnification and cellular details at high magnification. This module applies position-aware patch embedding and convolutions with various kernel sizes to extract features with different receptive fields, enhancing the model’s ability to process multilevel information and improving its representation capabilities. FSRM leverages the Hilbert transform to analyze the frequency domain of the image features, capturing both amplitude and phase information. This module helps identify subtle textural and structural variations that might be overlooked in spatial analyses, making it especially valuable for reducing the influence of staining techniques and color variations in pathology images. By incorporating frequency information, FSRM strengthens the model's ability to extract diagnostic features more effectively.", "questions": "1.\tThe font in the figure is somewhat small.\n2.\tThe caption for Figure 2 needs additional explanation.\n3.\tIn line 200, “Where” should be in lowercase.\n4.\tThe title \"3.3.2 METHODS\" should be changed to avoid confusing the audience.\n5.\tWill different backbone feature extraction models impact the performance of the proposed method? Why was ResNet18 chosen?\n6.\tWhy was a mini-batch size of 1 selected?\n7.\tThe data preprocessing steps and the choice of feature extraction model are not clear, considering that the proposed method depends on the extracted features.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper is well written and very clear. It is very easy to follow.", "weaknesses": "The reason for using ResNet18 as the feature extractor is not clear. The authors did not mention whether they tested different models.", "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": "", "extra_scores": {}}, {"review_id": "DwmZG2jS96", "reviewer": "Reviewer_3jEd", "summary": "This paper presents a Multi-Scale Frequency Domain Causal (**MFC**) framework for Multiple Instance Learning (MIL) applied to histopathology Whole Slide Imaging (WSI) which aims to improve both accuracy and generalisation by addressing three areas: data bias from unobservable confounders, integration of features across multiple scales, and interference from varying staining techniques. There are three main components, designed to address these issues: Causal Memory Intervention Module (**CMIM**), Multiscale Spatial Representation Module (**MSRM**) and Frequency-domain Structural Representation Module (**FSRM**). MFC is designed to be used on top of other MIL methods. The pipeline seems to consist of the following steps: \n\n- the MSRM takes as input N patches coming from WSI, pads them to an even number and reshapes them into a square, before passing this into three Conv2D operations (kernel 7, 3, 5) before unpadding, reshaping and keeping this output as a high resolution feature vector $X_{hl}$. $X_{hl}$ is then passed through 3 parallel Conv1D operation (kernel 16, dilation 1, 3, 5) to obtain N/16 low resolution feature vectors $X_{hl}$. These feature vectors are used as downstream input to FSRM. \n\n- The analytical signal of the above mentioned feature vectors is extracted by using the Hilbert Transform in parallel FSRMs. The processed features are mapped back to the original input space via a projection layer. \n\n- These features are input into parallel CMIMs, which initialise a set of trainable parameters with length k, which are then combined with attention-weighted inputs to select relevant memory elements and classified. \n\nThe authors implement their framework on top of 5 well-know MIL models and two benchmark datasets (CAMELYON16 and TCGA-NSCLC) and report an increase in performance across base model and datasets. They also compare with another causal framework IBMIL and perform ablation on the three proposed components.", "questions": "Based on the comments I have expanded upon above, here is a list of questions I believe the authors need to address. \n\n**MSRM**:\n\n- How exactly is the input from MIL models formatted for your framework?\n- What is the precise padding strategy in PPEG? Please provide implementation details.\n- How do you maintain spatial dimensions through the Conv2D operations?\n- How are 1D convolutions applied to the output of 2D convolutions?\n- Could you provide a detailed diagram showing the internal operations of PPEG?\n- What are the exact dimensions at each step of the MSRM pipeline?\n\n**CMIM**:\n\n- Could you provide the missing derivation showing how you get from Eq. 4 to 5?\n- How does selecting memory elements through attention implement the do-operator?\n- How do learned memory parameters act as mediators in your framework?\n- Could you mathematically justify why your memory-based approach implements front-door intervention?\n- How exactly is NWGM used to estimate the final equation?\n- What is the exact implementation of your attention mechanism?\n\n**FSRM**:\n\n- Is FSRM applied in RGB domain or feature space?\n- What are the exact input and output dimensions?\n- How exactly is the Hilbert transform applied to feature vectors?\n- How is the phase extraction implemented and used?\n- Could you explain how the analytic signal acts as a filter and what features it extracts?\n- Could you clarify the distinction between Hilbert Transform and analytic signal in your implementation?\n\n**Results**:\n\n- Could you provide uncertainty measures (e.g., standard deviation) for your results?\n- Why not compare IBMIL vs MFC across all baseline models?\n- How do you measure and validate that CMIM captures causal features?\n- How do you verify MSRM effectively captures information at different scales?\n\n**Framework**:\n\n- What is the correct order of operations - CMIM then MSRM, or MSRM first?\n- Could you provide a consistent diagram showing the exact flow of information?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "This paper presents some interesting and potentially valuable ideas for the field of computation pathology. The authors correctly identify three challenging areas: the potential presence of unobservable confounders in the data, the need to integrate local and global scales, and the noise introduced through variance in staining techniques. Their proposed framework attempts to address these challenges through a combination of targeted techniques: using a memory-based intervention for deconfounding, a multi-scale spatial representations for capturing both cellular and tissue-level features, and frequency domain analysis for reducing effects from variance in staining. The empirical results show consistent improvements across multiple baseline models and two different datasets, suggesting the potential value of this approach. The framework is designed to be modular and can be integrated with existing MIL architectures, which makes could make it useful to improve upon already existing approaches.", "weaknesses": "Unfortunately, I find this paper reads as if it wasn't properly finished. The explanation of the methods in general is unclear and disorganised, and in my opinion doesn't properly motivate the design choices. There's a lot of typos and inaccuracies and fundamentally, there's nothing to really back up the claims the authors are making which makes it hard to assess the strength/relevance of their contributions. \n\n**MSRM**: \n\n- How do you format the input coming from the MIL model? You say you perform MSRM as a first step. It requires N feature vector corresponding to image patches, so I assume this isn't from the output of the models? The Figure 1 implies it is, so this either needs to be amended or explained more clearly. \n\n- The padding strategy is not explained: you say \"X is padded in the PPEG\" but don't say how. You don't specify if/how you pad for the convolutions. You then say \"padding is removed, and the original dimensions are restored\", which doesn't explain how this is possible given the convolutions. The 1D convolution part is also unclear to me, as I don't understand how you apply the 1D convs to the output of 2D convolutions.\n\n- I also feel there are discrepancies between the visual illustration and the textual description. For example in Figure 2 (b) you graphically illustrate the MSRM module, but it seems to contradict the textual description: you just show a PPEG block with no details of internal operations, which splits into MaxPooling and three Conv1D, with additional GeLU and Linear layer. \n\nOverall, the description is confusing and lacks technical detail which would allow the reader to more easily understand your approach and why it makes sense.  \n\n**CMIM**: \n\nI read this section and was left wondering why this is a causal model with front-door intervention? First off, the paper skips the steps showing how the do-operator terms are manipulated to get from Eq. 4 to 5. It states the derivation is in the Appendix, but there is no Appendix... \n\nFurthermore, assuming eq. 5, why does it justify \"utilizing a memory module to estimate the overall distribution of the dataset during training and to refine the estimation of $\\hat{x}$ through attention-based sampling.\"? You then say this \"selected memory is further sampled and used as $\\hat{x}$ in the front-door intervention as illustrated in Figure 2 (a). Finally, we employ the Normalized Weighted Geometric Mean [...] to estimate the equation.\" \n\nI don't understand how this description or the illustration in Figure 2 (a) relates to a do-operator or front-door intervention. Selection via attention doesn't obviously implement the do-operator. Furthermore, how does a learned memory parameter acts as a mediator here? And how does this learned memory act as a front-door intervention? \n\nIt would be great if you could backup your claims more fully. This is one of the central aspects of this paper, so it needs to be carefully explained and argued.\n\n**FSRM**:\n\n- Line 234: \"By applying the Hilbert transform to a signal $x(t)$, we obtain a complex-valued function $x_a(t)$, where the original signal forms the real part, and the Hilbert transform provides the imaginary part.\" \n\nMy understanding is the Hilbert Transform takes a function of a real variable and produces another function of a real variable. Rather, here you obtain a complex-valued function by multiplying $\\hat{x}(t)$ by the imaginary unit $j$, which is you analytic signal $x_{a}(t)$. I think you're confusing the definition of Hilbert Transform with that of the analytical signal? \n\n- Line 264 - 268: \"The core of the module is the Hilbert transform, which extracts the analytic signal of the features, providing a comprehensive representation of both magnitude and phase information. An optional phase extraction step can be employed to focus specifically on the phase components, which often carry significant structural information.\"\n\nFigure 2 (c) seems to imply you apply FSRM in the RGB domain, but from the text above and Figure 1, it would seem this is applied in the feature space domain. In general, more clarity on how you're applying the analytical signal to your input would be appreciated. What are the input, output sizes? How exactly is the Hilbert transform applied to the feature vectors? How is the phase extraction used? Also, explaining how the analytical signal is being used as a filter on the images and what type of feature it can extract would also be helpful. \n\n**Results**:\n\n- Results are shown comparing this framework added on top of other MIL methods, but really this should be compared to other frameworks which also aim to reduce spurious correlations or increase generalisation. For example, you only compare (TransMIL + IBMIL) to (TransMIL + MFC), but you should compare IBMIL vs MFC in all cases.\n\n- There are no uncertainty measures on the results, so you can't make any claims as to the significance of the results. At a minimum you should be including standard deviation. \n\n- Line 404: you have no backing to the claim CMIM modules is more effective at capturing causal features. Why is it better? How can you illustrate this? \n\n- Line 429: again, you're not showing the MSRM module is actually effectively picking up information from different scales. \n\n- Line 456: FFT, DCT, and DWT are not even defined in the text.\n\n**Further comments**:\n\n- Line 211 - 213: \"After implementing CMIM, we further refined the estimation of the mediator, particularly by integrating low-magnification tissue information with high-magnification cellular information, which are both crucial in the diagnostic process.\" \n\nDo you implement CMIM, then MSRM as implied here - or MSRM first as implied in Figure 1. \n\n- Line 254: you're suddenly calling your FSRM module \"fdsr\".\n- Line 279: Metric\n- Line 302: MODELS \n- Line 308: CLAM isn't defined properly. \n- Line 316: the design of your train/test split should go in implementation details. \n- Line 320: Moreover, \n- Line 324: Camelyon16", "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": "", "extra_scores": {}}, {"review_id": "zm0eADduaL", "reviewer": "Reviewer_PUSw", "summary": "This paper proposes a multi-scale frequency-domain causal framework (MFC-MIL) for the classification of pathological images. The paper addresses the limitations of multiple instance learning (MIL) in whole-slide image (WSI) pathology analysis by identifying and tackling issues related to data bias and unobservable confounding variables. By incorporating causal intervention and multi-scale feature representation, the model demonstrates significant performance improvements across various datasets. Overall, the methodology is well-designed, demonstrates strong innovation, and is thoroughly validated. The MFC-MIL framework provides substantial practical and theoretical contributions, effectively enhancing the accuracy and robustness of pathological image classification.", "questions": "1. Does the memory selection mechanism in the CMIM module risk overfitting? The variation in the number of memory slots within the CMIM seems to significantly impact performance. Does this imply a potential risk of overfitting with the memory selection mechanism? Is this mechanism equally effective across other datasets or different types of pathology images?\n\n2. The paper presents two feature representation modules that enhance the model’s memory capabilities. The authors may consider referring to the following two papers for future work: *Unsupervised Multi-Domain Progressive Stain Transfer Guided by Style Encoding Dictionary* and *Wavelet Encoding Network for Inertial Signal Enhancement via Feature Supervision.* Both studies employ a regularization method based on Rényi entropy, which significantly enhances model representation and memory capacity. The authors could consider introducing this approach in future work. Such a design might improve the distinctiveness and information density of features, thereby increasing model stability under different confounding factors, minimizing interference from non-causal features, and making causal intervention more effective.\nThis suggestion is just an academic discussion and does not imply that the authors need to supplement any additional experiments. The sole purpose is to inspire the authors’ future work and model design. Adding a section on “Future Work” could further elevate the significance and impact of this paper.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The MFC framework is novel, combining multi-scale spatial and frequency-domain feature representations with a causal memory intervention module to address data bias in pathology image analysis. This multi-module, collaborative approach to causal intervention is particularly innovative.\n\n2. The MSRM module integrates information at both low and high magnifications, with multi-scale convolutions and positional encoding capturing tissue structures at various levels. The FSRM module effectively captures frequency-domain information through Hilbert transforms, introducing new structural features that improve the model's discrimination capability with complex data.\n\n3. The effectiveness of the MFC framework is validated on the Camelyon16 and TCGA-NSCLC datasets. Through comprehensive comparisons, ablation studies, and parameter investigations, the model’s performance is thoroughly evaluated, and the results are convincing.\n\n4. The proposed MFC framework surpasses existing methods in metrics such as accuracy and F1 score and also enhances model interpretability. The causal intervention module (CMIM) reduces the influence of non-causal features through a memory selection mechanism, offering a novel theoretical perspective for pathology analysis.", "weaknesses": "1. While the MSRM and FSRM modules demonstrate strong performance in experiments, further explanations regarding the theoretical motivations and details of each module would enhance clarity. For example, connecting the role of the Hilbert transform in frequency-domain feature extraction to specific pathology image characteristics would improve readers’ understanding of its biological significance.\n\n2. Although the ablation studies validate the effectiveness of each module, the analysis could delve deeper into how individual modules impact various evaluation metrics (e.g., AUC, F1 score). Adding a discussion on the underlying reasons behind the observed experimental phenomena could enhance the depth of the experimental results.\n\n3. The addition of the FSRM module improves classification performance, but the comparison with traditional frequency-domain methods (e.g., FFT) is somewhat brief. Providing a more detailed comparative analysis of different frequency-domain feature extraction techniques on pathology images could further highlight the practical effectiveness of the FSRM module.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)