HSC-T: B-ultrasound-to-elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis

Authors: hongcheng  han
zhiqiang  tian
qinbo  guo
jue  jiang
shaoyi  du
juan  wang
Venue: IEEE_org_EMBS_BHI_2024_Conference
Published: 2024-06-27 11:51:50

Abstract: Elastography ultrasound imaging is increasingly important in the diagnosis of thyroid cancer and other diseases, but its reliance on specialized equipment and techniques limits widespread adoption. This paper proposes a novel multimodal ultrasound diagnostic pipeline that expands the application of elastography ultrasound by translating B-ultrasound (BUS) images into elastography images (EUS). Additionally, to address the limitations of existing image-to-image translation methods, which struggle to effectively model inter-sample variations and accurately capture regional-scale structural consistency, we propose a BUS-to-EUS translation method based on hierarchical structural consistency. By incorporating domain-level, sample-level, patch-level, and pixel-level constraints, our approach guides the model in learning a more precise mapping from BUS to EUS, thereby enhancing diagnostic accuracy. Experimental results demonstrate that the proposed method significantly improves the accuracy of BUS-to-EUS translation on the MTUSI dataset and that the generated elastography images enhance nodule diagnostic accuracy compared to solely using BUS images on the STUSI and the BUSI datasets. This advancement highlights the potential for broader application of elastography in clinical practice. The code is available at https://github.com/HongchengHan/HSC-T.

Main paper: 
 i. introduction: 
 Ultrasound imaging, known for being non-invasive and costeffective, is the primary method for thyroid examination [1] . Elastography, an advancing technique, improves the assessment of soft tissue biomechanics. As a result, combining Bultrasound (BUS) with elastography (EUS) is increasingly emphasized in thyroid diagnostics [2] , [3] . However, elastography requires high-end equipment and skilled operation, limiting its use with portable BUS-only devices and in patients or settings where applying pressure is difficult. To address these issues, we propose a novel multimodal ultrasound thyroid nodule diagnostic pipeline based on Bultrasound-to-elastography translation. As Fig. 1(a) shows, it involves training a model with paired B-ultrasound (BUS) and real elastography (R-EUS) images from high-end equipment to enable it to learn the cross-modal mapping knowledge, then applying this model to obtain generated elastography images (G-EUS) in BUS-only scenarios. Combining BUS images and G-EUS images allows for improved diagnostic efficiency.Developing an accurate and efficient translation model is crucial for implementing advanced diagnostic pipelines. Current medical image translation methods [4] , [5] , typically using encoder-decoder networks or diffusion models with GAN Loss and pixel-level L1 or L2 losses, have shown success in tasks like CT-to-MRI translation. However, they face significant limitations in translating BUS to EUS. First, these methods commonly focus on reducing the distribution differences between generated and real images, neglecting inter-sample consistency and key feature differences crucial for diagnostic tasks. Second, due to the limited receptive field, pixel-level losses struggle to accurately capture the regionalscale local structural information essential for recognizing the attributes of nodules.In response to these challenges, we propose HSC-Translator (HSC-T), a novel BUS-to-EUS translation approach that leverages multi-level losses to establish hierarchical structural consistency constraints, as shown in Fig. 1(b) . Our method demonstrates superior proficiency in discerning both intersample consistency and distinctiveness of features through sample-wise cross-modal matching, thereby augmenting the diagnostic utility for thyroid nodule assessment. Furthermore, HSC-T enhances the representation of local structural details by adaptive patch-wise contrasting, resulting in enhanced precision in the translation from BUS to EUS. These advancements significantly elevate the diagnostic efficacy of the G-EUS in the context of thyroid nodule characterization. The main contributions of this study are as follows:• We introduce an innovative multimodal ultrasound diagnostic workflow for thyroid nodules, which learns crossmodal mappings from paired BUS and EUS images. This approach enables the generation of virtual EUS from BUS in hardware-constrained scenarios, thus enhancing diagnostic information. • Our novel BUS-to-EUS translation method leverages hierarchical structural consistency learning to improve inter-sample consistency, preserve local structural details, and achieve more accurate translation. • The proposed method demonstrates superior performance compared to competing methods in BUS-to-EUS translation on the MTUSI dataset, and significantly enhances nodule diagnostic accuracy by generating G-EUS images on the STUSI and the BUSI datasets.Elastography ultrasound (EUS) is plays an increasingly important role in clinical diagnosis by complementing traditional B-ultrasound (BUS). Qian et al. [2] combined BUS, EUS, and Doppler data to create a deep-learning-based breast cancer diagnosis system, which significantly improved alignment with BI-RADS guidelines. Qin et al. [3] demonstrated that integrating BUS with EUS greatly enhances the accuracy of thyroid nodule diagnosis compared to using BUS alone. Zhou et al. [6] highlighted the importance of EUS in diagnosing lung diseases, particularly in evaluating COVID-19 pneumonia. Xu et al. [7] showed that EUS videos provide reliable diagnosis of intrathoracic lymph nodes, improving clinical outcomes. Additionally, Shao et al. [8] used shear wave elastography (S-WAVE) to extract multimodal time series features for breast cancer detection, demonstrating its ability to accurately differentiate between malignant and benign breast lesions. This growing body of work underscores the clinical significance of EUS in improving diagnostic precision across a range of conditions.Elastography ultrasound offers clinical benefits but is limited by its reliance on high-end equipment and expert skill [9] . Since B-ultrasound (BUS) also captures some soft tissue biomechanics [10] , translating BUS into elastography ultrasound (EUS) presents a promising solution to these limitations.In response to the imperative of cost reduction, damage mitigation, and addressing the modality insufficiency in medical imaging, and driven by the advancements in computer vision's image synthesis methods [11] - [13] , image-to-image translation technology is progressively being employed for modality conversion of medical images [14] , [15] . For example, Kromrey et al. [16] achieved virtual elastography for liver fibrosis by deriving the shear modulus from diffusionweighted MRI. Özbey et al. [17] enhanced multi-contrast MRI and MRI-CT translation with adversarial diffusion models, improving performance through adversarial learning. Yao et al. [4] utilized conditional GANs (cGANs) for converting B-ultrasound to elastography, significantly improving breast cancer diagnosis accuracy. Bharti et al. [18] introduced QEM-CGAN to resolve training instability, lack of diversity, and mode collapse in medical image translation using evolutionary computation and multiobjective optimization.Current medical image translation methods focus on domain distribution or pixel-level errors but often overlook inter-sample feature relationships, crucial for BUS-to-EUS translation [19] . Pixel-level constraints, with limited receptive fields, fail to capture regional structural information needed for identifying nodule characteristics [20] . To improve BUS-to-EUS translation, enhancing both sample-level and patch-level feature learning is essential.Existing cGAN-based methods focus on minimizing overall distribution differences between generated and real data but overlook individual sample discrepancies, compromising the consistency of key personalized features. Pixel-level L1/L2 constraints, limited by their receptive field, also fail to capture regional feature consistency, leading to suboptimal outcomes and limiting the clinical use of G-EUS translated from BUS.To address these issues, we propose HSC-Translator (HSC-T) for BUS-to-EUS translation. HSC-T incorporates hierarchical structural consistency constraints at domain, sample, patch, and pixel levels to improve both sample-wise and region-wise consistency. The framework, shown in Fig. 2 , consists of two stages.In the first stage, the translator is trained on paired BUS and EUS data, guided by hierarchical structural consistency constraints through the cross-modal matching (CMM), adaptive patch contrast (APC), and conditional adversarial (CA) modules. As shown in Fig. 2 by an encoder-decoder translator, where the encoder generates a latent representation, and the decoder produces G-EUS. The CMM module ensures sample-level consistency (Fig. 2(c )) by minimizing the distance between paired BUS and EUS samples while maximizing it between non-corresponding pairs, enhancing the translator's ability to capture unique sample features. The APC module (Fig. 2(d) ) enforces regionwise consistency between G-EUS and R-EUS by performing patch contrast based on adaptive patch partitioning from pixel clustering. The CA module (Fig. 2(e) ) imposes domain-and pixel-level constraints using a discriminator and pixel-wise L2 loss. Finally, the objectives of CMM, APC and CA modules are combined into the overall loss function of HSC-T (Fig.In the second stage, as Fig. 2 (g) shows, the trained translator is applied to generate G-EUS from BUS without the need for paired elastography images. This synthetic G-EUS data serves as an additional input to a two-stream classification network, which consists of two CNN feature encoders (both based on ResNet-34 [21] ) and an MLP classifier. The integration of G-EUS significantly enhances the diagnostic performance by providing complementary elasticity information.The cross-modal matching module is proposed to minimize the distance between each BUS sample and its corresponding EUS while maximizing the distance from non-corresponding EUS samples, thereby guiding the translator to effectively learn and distinguish the unique features of each pair of samples.As Fig. 2 (c) shows, first, the feature encoder G E Enc is constructed to obtain the latent representation of the elastography, which shares an identical structure with the feature encoder G Enc in the translator. The feature maps of the input BUS and the EUS are calculated by z B = G Enc (x B ) andz E = G E Enc (x E ).Then, they are converted to the query vector q and the key vector k by global pooling operation, respectively. Sample-wise cross-modal matching is achieved by the contrast calculating between q and k. Due to the large number of samples in the training dataset, the contrast calculation is conducted within a mini-batch to ensure computational efficiency. Sample-wise structural consistency loss L Sample is calculated asEQUATIONwhere N indicates the number of samples in the mini-batch, q means the feature vector of the input BUS, k i indicates the feature vector of the i-th EUS in the mini-batch, and the k + represents the feature vector of the EUS that is associated with the input BUS. A scaling coefficient τ is introduced to normalize the value of q •k within an appropriate range, which is set to 0.05.To address the challenge of maintaining consistent feature extraction during end-to-end training, where the separate parameter updates of encoder G E Enc and G Enc through gradient backpropagation can lead to optimization instability and increased computational overhead, momentum update [22] is employed for optimizing the parameters of the two encoders. The parameters of G E Enc and G Enc at the t-th step are calculated byEQUATIONEQUATIONwhere θ t and θ E t are the parameters of G Enc and G E Enc , separately. η t is the learning rate. L indicates the overall loss and m is the momentum coefficient, which is set to 0.99.Sample-wise cross-modal matching establishes the intersample correlations between different samples, thereby enhancing the model's attention to salient sample-specific features and improving the accuracy and clinical applicability of the elastography generated from B-ultrasound images.Pixel-level L1 and L2 losses in cGANs evaluate errors independently for each pixel, missing inter-pixel relationships and often failing to capture regional-scale features needed for accurate BUS-to-EUS translation. To address this, we propose an adaptive patch contrast module that improves interpixel relationship modeling, enhancing structural consistency between G-EUS and R-EUS.As Fig. 2 (d) shows, first, pixel clustering based on simple linear iterative clustering (SLIC) [23] is performed to the R-EUS x E to obtain the adaptive patch partition P(x E ) = {p 1 , p 2 , ..., p n }, the number of patches n is set to 16. Second, G-EUS and R-EUS are divided into patches according to the partition P(x E ). Compared to the square divided patches, the patches obtained through adaptive pixel clustering not only consider the positional relationship but also incorporate pixel correlation, thereby augmenting the semantic and perceptual significance when assessing local structural consistency between G-EUS and R-EUS. Then, each patch of G-EUS and R-EUS is flattened and resized to a uniform size. Subsequently, the flattened patches of G-EUS and R-EUS are fed into two multi-layer perceptrons (MLP) respectively, to obtain the latent representations for patch-wise contrast learning, the patchlevel loss is calculated byEQUATIONwhere n indicates the number of patches, u represents the feature vector of one of the patches in G-EUS, v i indicates the feature vector of the i-th patch in R-EUS, and v + represents the feature vector of the patch in R-EUS that is located at the same position as u. τ is the scaling coefficient introduced to normalize the value of u•v within an appropriate range, which is set to 0.05. The adaptive patch-wise contrast not only enhances crosspixel correlation through pixel-clustering but also guides the model in extracting personalized structural information from each patch of EUS through patch-wise contrast learning, thereby improving the preservation of patch-level consistency, which is essential for accurate BUS-to-EUS translation.To establish domain-level constraints and pixel-level constraints, we introduce the conditional adversarial module, as Fig. 2 (e) shows. The R/G discriminator incorporates a lightweight CNN classifier designed to accurately differentiate between R-EUS images and G-EUS images, performing adversarial learning with the translator via a gradient reversal layer. It supervises the translation model to generate EUS images that are indistinguishable from real data, ensuring the domain-wise distribution consistency of G-EUS images and R-EUS images in terms of color, texture, structure, etc. The domain-level loss is calculated byEQUATIONwhere D indicates the domain discriminator, and G represents the translator, thereby xE = G(x B ).Subsequently, for preserving pixel-level structural information, L2 loss to narrow the pixel-wise Euclidean distance between G-EUS images and the corresponding R-EUS images, The pixel-level loss is calculated byEQUATIONwhere h and w represent the height and the width of the image.The proposed HSC-T supervises the translator's learning process through hierarchical structural consistency constraints, with the final optimization target depicted in Fig. 2(f ). Domain-level loss L Domain , sample-level loss L Sample , patch-level loss L P atch , and pixel-level loss L P ixel are established using the CA module, the CMM module and the APC module, respectively. To integrate the structural consistency constraints across all four levels effectively, the overall loss function L HSC is defined as a weighted sum of L Domain , L Sample , L P atch and L P ixel , expressed as L HSC = L Domain +λ 1 L Sample +λ 2 L P atch +λ 3 L P ixel , (7) where λ i (i = 1, 2, 3) are hyper-parameters used to harmonize the influence of four components on optimization, they are set to λ 1 = 0.4, λ 2 = 0.8, λ 3 = 0.4. Malignant cases in the MTUSI and the STUSI include thyroid papillary carcinoma, micro-papillary carcinoma, and micro-follicular papillary carcinoma, some with calcification. Benign cases include cystic degeneration, thyroid nodules, and follicular tumors, with a portion also exhibiting calcification.2) Implementation Details: The datasets were split into training, validation, and testing sets (5:2:3 ratio), with consistent proportions of benign and malignant samples. Threefold cross-validation was performed to ensure model stability, using different partitioning strategies for each experiment. In the image translation experiments, the sizes of input BUS images and output EUS images were set to 256 × 256. In the nodule diagnosis experiments, the sizes of BUS images and G-EUS images were set to 224 × 224. RMSProp optimizer is used for optimization, the initial learning rate was set to 0.001, the momentum was set to 0.95, and the weight decay was set to 1e-5. Random rotation, random cropping, and vertical and horizontal flipping were performed on the input B-ultrasound images and the corresponding ground truth elastography images for data augmentation in training.3) Evaluation Metrics: For image translation evaluation, two full-reference metrics are used: structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). SSIM assesses image similarity in terms of luminance, contrast, and structure, simulating human visual perception. PSNR measures image quality by comparing the signal-to-noise ratio between real and generated EUS images. Nodule diagnosis performance is evaluated using accuracy (Acc.) and F 1 score. Higher Acc. indicates better overall classification, while F 1 provides a balance between sensitivity and precision.The proposed method is compared with recent influential work in the field of image-to-image translation and medical image modal conversion, including GAN-based methods [4] , [11], [12] , [14] , [25] and also diffusion model-based methods [13] , [17] . The quantitative results of various methods in the B-ultrasound-to-elastography translation task on the MTUSI dataset are presented in Table II and Fig. 3 .P i x 2 P i x A t t G A N M e d G A N S A G A N D D P M V -E U S S y n D i f f H S C -T ( o uThe results in Table II are presented in the format of mean ± std., which represents the mean value and standard deviation of the results obtained through cross-validation. The HSC-Translator outperforms existing methods in SSIM and PSNR metrics for both benign and malignant nodules, highlighting its effectiveness in improving BUS-to-EUS translation accuracy. Diffusion-based methods, DDPM [13] and SynDiff [17] , perform relatively weakly, indicating the limitations of diffusion models in BUS-to-EUS mapping. The V-EUS [4] method ranks second, showing strong performance and suggesting that models for breast and thyroid ultrasound images can generalize across modality conversion tasks. Additionally, Fig. 3 shows the distribution of SSIM indicators for each sample on the MTUSI dataset under different BUS-to-EUS translation methods. The proposed approach demonstrates superior performance and stability in the BUSto-EUS translation task, as evidenced by its higher mean and median SSIM values, along with a narrower range between samples.The qualitative results of a few samples are visualized and presented in Fig. 4 . Compared to the competitive methods, the EUS generated by the proposed approach better preserves the overall structural information of the input B-ultrasound image and exhibits local structural details that closely resemble those of real EUS. It demonstrates that the proposed approach effectively enhances the model's capacity to acquire more robust structural consistency information, leading to improved accuracy in BUS-to-EUS translation. Nodule diagnosis experiments were conducted to assess the impact of the generated EUS on diagnostic accuracy. The diagnostic network was trained with BUS and the G-EUS generated by different methods. To control for the network's structure, a baseline (no EUS) was tested by using BUS images in both branches. The another control (with R-EUS) compared the diagnostic efficacy of real versus generated EUS. Additionally, the cross-dataset experiments were performed to evaluate generalizability by training on the MTUSI and testing on the STUSI.The nodule diagnosis performance of various translation methods is shown in Table .III. The introduction of G-EUS effectively enhances the diagnostic accuracy compared to solely utilizing B-ultrasound images, with the degree of improvement being positively correlated with the image translation effect demonstrated in Table II . HSC-T yields a 13.2%↑ increase in Acc. and a 16.0%↑ enhancement in F 1 score on the STUSI dataset, while achieving a 6.7%↑ improvement in Acc. and an 11.1%↑ boost in F 1 score on the BUSI dataset, which outperforms the other methods. Additionally, the G-EUS generated by the proposed HSC-T exhibits a substantial enhancement in diagnostic performance that is comparable to the contribution achieved through direct utilization of R-EUS for diagnosis and exhibits significant generalizability in the experiments across the MTUSI and the STUSI datasets. Through t-tests conducted in four experimental groups, the pvalues of the accuracy of the diagnostic model based on HSC-T compared to the diagnostic model without the use of EUS are 0.0013, 0.0014, 0.0021, 0.0012, respectively, which means the improvement in diagnostic performance brought by the G-EUS generated by HSC-T is statistically significant. Moreover, as depicted in Fig. 5 , the confusion matrices further validate the potential of HSC-T in addressing the issue of missing modality within clinical practice. Furthermore, the receiver operating characteristic (RoC) curves of G-EUS-assisted nodule diagnosis through various BUS-to-EUS translation methods are shown in Fig. 6 , HSC-T achieves the highest AUC. The above findings suggest that the G-EUS generated by the proposed method can significantly enhance the diagnostic efficacy of single-mode ultrasound equipment for thyroid and breast nodules.To investigate the impact of the proposed CMM module and APC module, ablation analysis was conducted on the MTUSI dataset by individually removing the CMM module and APC module from the model. The results are presented in Table IV , where the check mark (✓) indicates that the corresponding module is included in the model. Compared to the baseline, the inclusion of the CMM module resulted in an improvement of 2.80%↑ in SSIM and 1.85↑ in P SN R. Similarly, the inclusion of the APC module led to an enhancement of 2.36%↑ in SSIM and 1.53↑ in P SN R. Notably, when both modules were combined, there was a significant increase of 4.43%↑ on SSIM and 2.33↑ on P SN R. These findings demonstrate that both the CMM module and the APC module effectively improve translation from BUS images to EUS images. Moreover, we also attempted to eliminate the CA module. However, this led to a significant decline in performance. This observation represents the effectiveness of using cGAN as the baseline for BUS-to-EUS translation. Based on the learning paradigm of cGAN, the proposed CMM and APC modules enhance the performance of image-to-image translation.The model's bias towards sample-level, patch-level, and pixel-level consistency is controlled by λ 1 , λ 2 , and λ 3 in the loss function L HSC . To evaluate the effect of the proposed CMM module, APC module and CA module, these parameters were varied during training to examine their impact on model performance on BUS-to-EUS translation.The results for 49 (7×7) different combinations of λ 1 and λ 2 are displayed in Fig. 7 (a). Initially, increasing these values improves the structural similarity index (SSIM), but excessively large values lead to performance degradation. A significant discrepancy between λ 1 and λ 2 also causes a noticeable drop in SSIM, showing that focusing too much on either sample-level or patch-level consistency negatively affects translation. Additionally, when both parameters are too high, domain-level and pixel-level consistency are neglected, further reducing performance. The optimal performance is observed when λ 1 = 0.4 and λ 2 = 0.8, indicating that this combination strikes the best balance between sample-and patch-level consistency for BUS-to-EUS translation.To further explore the role of pixel-level constraints, Fig. 7 (b) presents the impact of varying λ 3 , which governs pixellevel consistency while fixing λ 1 = 0.4 and λ 2 = 0.8. SSIM drops significantly when λ 3 is either too small or too large. A small λ 3 weakens the pixel-level constraint, while a large one causes overfitting by prioritizing pixel-level consistency over other levels. The optimal setting for λ 3 is 0.4.In this study, we propose a novel approach for translating B-ultrasound (BUS) images into elastography (EUS) images to assist in the diagnosis of thyroid nodules. Our experimental results demonstrate that the proposed HSC-Translator effectively enhances the learning of structural consistency at different levels between BUS and EUS images. This leads to a significant improvement in the performance of BUS-to-EUS translation compared to existing methods. Additionally, when applied to portable single-modal ultrasound devices, the generated elastography (G-EUS) images produced by the HSC-Translator provide more biomechanical properties information and enhance the accuracy of thyroid cancer diagnosis compared to using BUS data alone. The ability to generate high-quality G-EUS images from BUS using our method shows promise for improving diagnostic capabilities in resource-limited settings, where access to high-end ultrasound equipment may be restricted. 
 
 Appendix:  



Reviews: review: title: title: HSC-T: B-ultrasound-to-elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis
paper_summary: paper_summary: This paper involves training a model with paired B-ultrasound (BUS) and real elastography (R-EUS) images from high-end equipment to enable it to learn the cross-modal mapping knowledge, then applying this model to obtain generated elastography images
(G-EUS) in BUS-only scenarios.
strength_weakness: strength_weakness: Lack of details regarding the size of training samples and testing samples.
questions: questions: Data are collected from three medical devices. Will the type of medical device impact the BUS-to-EUS translation performance and nodule diagnostic accuracy? How would the performance be if we used an unseen medical device?
title: title: The manuscript presents a novel model for B-ultrasound-to-elastographyto translation, and improves the thyroid cancer diagnostic accuracy.  The method is novel, the problem to be solved is important, and the model validation analysis is sufficient.
paper_summary: paper_summary: The manuscript introduces a novel model designed to translate B-ultrasound images into elastography, with the aim of enhancing diagnostic accuracy for thyroid cancer. The proposed approach integrates four level constraints: domain-level, sample-level, patch-level, and pixel-level constraints. The proposed model, using BUS-GEUS, enhanced nodule diagnostic accuracy.
strength_weakness: strength_weakness: The manuscript should consider the issue of sample imbalance in the dataset used for classification. An unbalanced dataset can significantly impact the experimental results. It would be beneficial to include an analysis of how this imbalance affects the model's performance, perhaps using a confusion matrix. If the imbalance has a notable effect, the authors may need to consider strategies to mitigate it.

The ablation study is a critical component of validating the model. The authors should also consider extending this analysis to include domain-level and pixel-level evaluations, which could provide further insights into the model's performance and the effectiveness of the different constraints. The manuscript should also discuss the impact of the loss bias coefficient, including Lamda3, in more detail. Understanding the role of this coefficient is essential for assessing the model's optimization process.

To enhance the impact and reproducibility of the study, the authors are encouraged to make the code and model publicly available. This would allow others in the field to validate and build upon their work.

A more thorough comparison with existing models and methods in the literature is necessary. This would help position the proposed model within the broader context of thyroid cancer diagnosis and demonstrate its advantages over current approaches.
questions: questions: why not use the MTUSI dataset in the nodule diagnosis experiment? 

Clarification is needed on whether the model is trained separately for each dataset or if the STUSI and BUSI datasets share the same model parameters. This information is crucial for understanding the generalizability and robustness of the model.
title: title: HSC-T: B-ultrasound-to-elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis
paper_summary: paper_summary: This work develops an image synthesis model to translate B-mode ultrasound (BUS) to elastography ultrasound (EUS) by designing a sophisticated loss function that considers matching at different levels, including domain-level, sample-level, patch-level, and pixel-level. The authors also demonstrate the added value of translating BUS to EUS for thyroid lesion classification.
strength_weakness: strength_weakness: If the ultimate goal is to improve diagnostic accuracy, it is unclear why an intermediate step of translating BUS to EUS is necessary. Since EUS is synthesized from BUS, it supposedly will not generate more information than the original BUS images. Although the authors demonstrated the value of including synthesized EUS in lesion diagnosis, it is unclear whether the model using only BUS images is truly optimized. Some clarification on the need for EUS translation is desired.

Additionally, statistical tests are needed to demonstrate the value of the proposed method. Please clearly describe the disease when discussing the datasets.
questions: questions: none