Anatomy-aware Representation Learning for Medical Ultrasound
Authors: Seok-Hwan Oh
Myeong-Gee Kim
Guil Jung
hyeonjik lee
Young-Min Kim
Sang-yun Kim
Hyuksool Kwon
Hyeonmin Bae
Venue: ICLR 2026 Poster (2026)
Keywords: Foundation model
medical ultrasound
representation learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: An anatomy-aware representation learning in medical ultrasound, introducing a large scale medical ultrasound dataset

Abstract: Diagnostic accuracy of ultrasound imaging is limited by qualitative variability and its reliance on the expertise of medical professionals. Such challenges increase demand for computer-aided diagnostic systems that enhance diagnostic accuracy and efficiency. However, the unique texture and structural attributes of ultrasound images, and the scarcity of large-scale ultrasound datasets hinder the effective application of conventional machine learning methodologies. To address the challenges, we propose Anatomy-aware Representation Learning (ARL), a novel self-supervised representation learning framework specifically designed for medical ultrasound imaging. ARL incorporates an anatomy-adaptive Vision Transformer (A-ViT). The A-ViT is parameterized, using the proposed large-scale medical ultrasound dataset, to provide anatomy-aware feature representations. Through extensive experiments across various ultrasound-based diagnostic tasks, including breast and thyroid cancer, cardiac view classification, and gallbladder tumor and COVID-19 identification, we demonstrate that ARL significantly outperforms existing self-supervised learning baselines. The experiments demonstrate the potential of ARL in advancing medical ultrasound diagnostics by providing anatomy-specific feature representation

Published as a conference paper at ICLR 2026
ANATOMY-AWARI REPRESENTATION LEARNING FOR MEDICAL ULTRASOUND
Seok-Hwan Oh Barreleye Inc. Scoul, Korea
Myeong-Gee Kim Barreleye Inc. Scoul, Korea
Guil Jung
Hyeon-Jik Lee KAIST Daejeon, Korea
Young-Min Kim
KAIST
KAIST Daejeon, Korea
Daejeon, Korea
Sang-Yun Kim
Hyuksool Kwon
Hyeon-Min Bac"
KAIST Daejeon, Korea
SNUBH
KAIST Daejeon, Korea
Seongnam, Korea
ABSTRACT
Diagnostic accuracy of ultrasound imaging is limited by qualitative variability and its reliance on the expertise of medical professionals. Such challenges in- crease demand for computer-aided diagnostic systems that enhance diagnostic ac- curacy and efficiency. However, the unique texture and structural attributes of ultrasound images, and the scarcity of large-scale ultrasound datasets hinder the effective application of conventional machine learning methodologies. To address the challenges, we propose Anatomy-aware Representation Learning (ARL), a self-supervised representation learning framework specifically designed for medi- cal ultrasound imaging. ARL incorporates anatomy- Vision Transformer (A-VIT). The A-VIT is parameterized, using the proposed large-scale medical ul- trasound dataset, to provide anatomy- feature representations. Through tensive experiments across various ultrasound-bart -based diagnostic tasks, including breast and thyroid cancer, cardiac view classification, and gallbladder tumor and COVID-19 identification, we demonstrate that ARL significantly outperforms ex- isting self-supervised learning (SSL) baselines. The results demonstrate the poten- tial of ARL in advancing medical ultrasound diagnostics by providing anatomy- specific feature representation.
INTRODUCTION
Medical ultrasound (US) is one of the most widely used medical imaging modalities. US is pre- ferred modality for the early diagnosis of various discases due its non-ionizing, cost-effective, and real-time nature. Recently, the application of artificial intelligence (AI) in medical diagnostics has object recognition, segmentation, and image classification technologies over the past few years have enabled more accurate and effi- cient detection and diagnosis of disease, including lung disease diagnosis from computed tomogra- phy (CT) scans and breast tumor identification in mammography (Suganyadevi et al. (2022)). Med- ical US is often characterized by limited image quality and significant qualitative variations, mak- ing diagnostic accuracy heavily reliant on the expertise of medical professionals (Oelze & Mamou (2016)). Such reliance has driven a growing demand for cmmpteer-assiste diagnosis systems capa- ble of providing quantitative assessments in medical US and enhance diagnostic precision (Akkus) ett al. 2019); Wang et al.) (202T)).
increased significantly (Kumar et 2023) Advancements
However, the availability of medical US data is limited due to several challenges, including the high cost and logistical complexities of acquiring annotated datasets, as well as privacy concerns and reg- ulatory restrictions in medical imaging (Paleyes al. (2022); Mazurowski al. 2019)). Compared to computer vision and natural language processing (Deng et al. (2009); Kirillov et al. (2023): Zhou al. (2017)), the number of available medical US datasets is considerably smaller. For instance, the open-access breast US image dataset (Al-Dhabyani et al. (2020); Gomez-Flore et al. (2024)
"Corresponding author

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Published as a conference paper at ICLR 2026
and the thyroid US dataset (Pedraza al. (2015a) contain less than thousand annotated images. The scarcity of data presents significant challenges in training Al models, as their performance and generalizability constrained. US image attributes are affected by variations in equipment speci- fications, imaging techniques, and probe characteristics, which makes them particularly prone to the domain shift problem (Ortiz et al. (2012)) As result, model trained the dataset with limited diversity may perform poorly on another dataset collected under different conditions.
In computer vision, self-super visual representation learning has demonstrated its effectiveness in capturing general image attributes. Pre-trained models generated from the representation learning are then utilized for downstream tasks, achieving robust performance even with limited data. Recent udvancements, such as DINO (Caron et al. (2021) ) and MAE (He et al. (2022), have introduced self- supervised learning methodologies for natural images (NT), demonstrating their effectiveness across wide range of NI downstream tasks. However, we observed that pre-trained models based on NI datasets fail to perform effectively on US-related downstream tasks. Such performance gap arises from the substantial differences in image attributes between NIs and medical US images, which vary significantly in terms of texture, noise, and structural characteristics. For instance, medical US images typically exhibit a speckled texture caused by the physical properties of sound waves interacting with tissues, which is absent in NIs (Damerjian et al. (2014))
To address the challenge, we propose visual representation learning scheme designed for medical US. In this study, we have curated one of the largest-scale datasets dedicated to medical US, which serves the foundation for the proposed representation learning. Medical US images present dis- tinct attributes that vary significantly depending on the organ being imaged. To address the chal- lenges, we introduce Anatomy-aware Representation Learning (ARL), a framework that utilizes an anatomy-condiitonne deformable transformer (ACDT) to ensure that representation learning is tai- lored to the specific organ or anatomical region being imaged. Through extensive experiments, we demonstrate that ARL effectively facilitates representation learning, enabling application across a wide range of medical US tasks.
RELATED WORK
Medical Ultrasound. Medical US is essential diagnostic tool for variety of medical condi- tions, including breast, thyroid, and gallbladder cancer, and cardiac and lung disease. In breast US real-time observation of breast masses (Cao al. (2017)) and identification of malignancy (Cheng iet al. (2010)) are increasingly supported by Al-based techniques. The Al-driven methods not only reduce operator dependency but also significantly improve diagnostic accuracy (Shen et al. (2021) Similarly, thyroid US plays a pivotal role in detecting thyroid cancer (Wang et al. (2019)). The use of learning-base based approaches to analyze the radiological features of thyroid tumors and deter- mine the malignancy has grown, leading to enhanced diagnostic precision and reliability. US is widely applied cardiac care due to its ability to provide real-time imaging of the beating heart. In echocardiography (Echo), deep learning models employed to classify echocardiographic views and evaluate cardiac functionality, enabling more efficient and accurate cardiac assessments (Madani et al. (2018)). During the COVID-19 pandemic, medical US emerged as a critical tool for detecting lung abnormalities. In particular, the identification of abnormal symptoms in lung US was increas- ingly facilitated by machine sarning-based approaches (La Salvia et al. (202I)). Additionally, US plays a significant role in detecting gallbladder tumors, supporting the early-stage identification of tumors, and improving clinical outcomes (Basu al. (2024)..
Self-supervised learning (SSL). SSL methods are designed to learn meaningful representations by leveraging supervisory cues derived from unlabeled data. Instance discrimination one of the lead- ing paradigms, which enforces consistency across augmented views of an image. Instance discrim- ination includes contrastive learning and non-contrastive learning. In contrastive learning, methods such as SimCLR (Chen et al. (2020)) and MoCo (He et al. (2020)) learn to discriminate positive pairs from negative pairs. The objective is to bring similar representations closer together while pushing dissimilar ones apart. However, the approaches necessitate the use of a large number of negative pairs, which limits the widespread use of the frameworks. In contrast, con-contrastive learning methods, including (Yrll et al. (2020)), SimSiam (Chen & He (202I), and DINO, propose to maximize the similarity between augmented views without relying on negative samples. No- tably, DINO introduces self-distillation mechanism via matching loss, which has demonstrated
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Ultrassund Spectfic Characteristes
Representation learning netoratul
Image lp Cr practerrab atc:
Brayy acall and low color variation
Anatomy specific image attr Bute
Unrssaund Image
Nstural imiga
Thoal
Bccol
Hiresourd
SCA Festire
Proposing lnnee-scale
104
of
101
100
Figure 1: (a) Difference between medical US and natural images. (b) PCA of the features using
Dino v3 and the proposed scheme.(c) Distribution of the proposed large-scale US dataset.
significant potential across variety of computer vision tasks. addition, masked autoencoders (MAE) have emerged as another significant approach for SSL. Inspired by SSL methodologies in natural language processing (NLP), MAE learns representations by masking portions of an image and training the model to reconstruct the missing content (He et al. (2022); Feichtenhofer et al. (0022). MAE representation learning encourages the model to extract meaningful features from the contextual information provided by the unmasked regions, further enhancing its ability to learn robust representations.
Self-supervised learning in medical image. In the medical domain, there is growing trend toward building modality-specific foundation models via SSL on large scale medical datasets (Kang et al. (223b)). Kang et al. (2033)) assembled extensive pathology image repository and proposed domain aligned representation learning framework that surpassed traditional baselines on multiple downstream pathology tasks. Likewise, the MIS-FM model (Wang al. (2023)), pre-trained three-dimensional computed tomography datasets, achieved state-of-the-art multi organ segmenta- tion performance. Together, these studies highlight the potential of SSL-derived foundational mod- els when adapted to the specific characteristics of each imaging modality. As Al applications in the US domain continue expand, the development of US foundation model, capable of support- ing broad spectrum of downstream tasks, will facilitate the delivery of higher quality Al assisted healthcare.
Self-supervised learning with anatomical features. Recently, there has been growing interest SSL frameworks that incorporate anatomical characteristics observed medical images. Jīao et al. (2020) propose self-supervise, framework that leverages fetal ultrasound videos to capture anatomical characteristics inherent to the fetal imaging domain. Fu ct al. (2022) further exploit fetal- specific anatomical cues by constructing anatomy-awes -wware pretext objectives to enhance representation learning within the domain. Hu et al. (202)) introduce methodology that enforces anatomical con- sistency across multi-modal medical data, thereby preserving shared struetural information across modalities. While these approaches demonstrate the value of incorporating anatomical cues into SSL, they are primarily designed for single anatomical domain. However, medical ultrasound hibits anatomical heterogeneity, where diagnostically meaningful features vary substantially across organs. To obtain generalizable representations applicable across anatomy categories, we propose an SSL framework that learns anatomy-speciio feature attributes over wide range of organs, ac- counting for the inherent variability of medical ultrasound.
METHOD: ANATOMY-AWARE REPRESENTATION LEARNING
3.1 DIFFERENCE FROM NATURAL IMAGES
SSL has primarily been developed and evaluated using large-scale NI datasets. However, medical US images exhibit unique characteristics that significantly differ from those of NIs. Consequently, demonstrated in (bb), SSL models trained on NI datasets perform poorly when applied to US

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imaging tasks. This section presents the key features that distinguish US images from general NIs, demonstrating the challenges associated with transferring SSL to the medical US domain.
Image speckle characteristics. One of the notable differences between medical US and NIs lies in their speckle characteristics. In NIs, adjacent pixels generally exhibit minimal variance due to smooth gradients and consistent textures. In contrast, US images dominated by significant speckle noise. The noise arises from the interaction of sound waves with tissue structures, producing granular patterns throughout the image (Matrone et al. (2014)) Speckle patterns are not merely arti- facts but are often leveraged as essential features in diagnostic applications, such as assessing tumor malignancy (Tsui et al.] (2010)) or evaluating cardiac functionality (Mondillo et al. (201T). How- ever, SSL methods trained on speckle-free NIs tend to diminish the importance of speckle-related features, leading to suboptimal performance on US-specific downstream tasks. Such limitations demonstrate the necessity of developing SSL. approaches tailored specifically to medical US data.
Gray-scale and low color variation. Another key difference lies in the limited color variation of US images. Unlike NIs, US images are mainly gray-scale with low pixel intensity variation. Such constrained color and intensity range poses additional challenges when applying models trained on NI datasets to US data, as these models are often optimized to leverage the rich color variations that are absent in US images.
Anatomy- sppecifc image attribute. Medical US images captured for diagnostic purposes and are typically examined on the related human organs. Consequently, the images are inherently influ- enced by the anatomical boundary condition and structural characteristics of the targeted organ. The meaningful feature of medical US images varies substantially depending on the organ being exam- ined, as each organ and lesion type exhibits histologic heterogeneity, reflecting its unique anatomical and functional characteristics. Therefore, it is essential to design representation learning methods that are tailored the specific organ being imaged to ensure meaningful and accurate feature extrac- tion. To address the challenge, we propose ARL, a novel approach that incorporates the anatomical context of the imaged organ into the learning process. The proposed framework aims improve both the accuracy and generalizability of self-supervised models, thereby advancing their applica- bility in medical US imaging tasks.
3.2 LARGE-SCALE MEDICAL ULTRASOUND DATASET FORMATION
Effective SSL relies on large-scale datasets to enable neural networks learn robust and general- izable features. To facilitate the development of the proposed ARL framework, we configured a comprehensive medical US dataset comprising 5.2 million images aggregated from diverse sources, including publicly available datasets and multiple medical institutions. Specifically, the dataset in- corporates data from 11 publicly available datasets, as well as US images from 12 medical institu- tions in the United States, 2 in South Korea, and 1 in India. The detailed description of the dataset introduced Appendix A. The dataset is organized into 16 distinct anatomical categories (Fig. (c)).. The dataset includes images acquired using linear, convex, and sector array probes. The image pixel resolution ranges from 64x64 to 1280x960, with the height and width distributions character- ized by 503.1 167.1 and 655.8 + 238.2 (mean + std), respectively. The dataset covers a broad span of imaging depths up to 24 cm. By aggregating a diverse and extensive collection of US data from different geographic regions and clinical conditions, the dataset captures wide spectrum of imag- ing variations. Such diversity ensures a more representative and comprehensive dataset, significantly enhancing the potential for effective SSL in medical US applications.
3.3 ANATOMY-AWARE VISION TRANSFORMER (A-VIT)
In this work, propose the V-V model designed to provide anatomy-adaptin SSL for US images by incorporating anatomical context. A-ViT extends the standard ViT (Dosovitskiy (0200) by integrating an unatomy-conditionn deformable transformer, which adapts feature extraction to the unique characteristics of specific anatomical regions, enabling context-aware representations.
As illustrated in Fig. 2, the model begins by processing the spatial US image through standard patch embedding. This is followed by a series of transformer blocks that perform feature extraction and representation learning. To account for the unique anatomical structures that vary across different
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Studint
Unrmaard Frage
Seetiy S Nivon
Adversarial Loss (Generator Play
teotndleg
Corrigt imagge
cth
1079
Z5
Pos
MAE Decoder
Distriminator (ACOT)
Owetedding
Distillation
Ap
EMA
(Olscriminator 45oy
Zt
Figure 2: Overall configuration the proposed A- A-VIT.
imaging regions, A-VIT introduces an mmatomy-specioo embedding, ensuring that the model adapts the representation learning process to the targeted anatomical context.
Inatomy-Conditione Deformable Transformer (ACDT). The spatial distribution of diagnos- tically meaningful features in US images depends strongly the organ being imaged. For example, Echo typically relies on globally distributed features such as cardiac chambers, whereas breast US emphasizes locally confined features such breast lesions. To effectively encode these anatomy- dependent spatial relationships among image patches, ACDT employs deformable convolution with offsets conditioned anatomical context.
Formally, the flattened patch embeddings Iy E NxCxP2 are reshaped into 2D spatial patches IP E N XxCxxxp where denotes the channel dimension and P x the patch resolution. Sixteen representative anatomical conditions, corresponding to key diagnostic targets in medical US, are encoded as one-hot vectors of dimension a E (0. 1) BX16 These vectors are projected through learned embedding layer produce the anatomical context representation AC BBC which is added to the patch tokens as P,AC = xp + AC'.
The PCACC are then used to determine the kernel offsets Aps go(xp,AC). Given kernel weights Wr and K sampling locations, the deformable convolution is defined as
K yp(p) Σ
wk S(EP, p + App),
where -(.,)) denotes bilinear sampling. Through the mechanism, ACDT adaptively adjusts its ceptive field according to the anatomical condition, enabling more accurate and context-sensitive feature extraction for diverse US imaging tasks. Following the deformable convolution, the anatomy-conditioned features yp are integrated into the transformer block through the multi-head attention mechanism. In this formulation, the mratomy-conditionel output yp serves the key and value, while the original patch embeddings x are retained as the query:
(gpW))ypW*>) Vdl.
DeformAttu(ap.ur) = Softmax
(yPWV),
where Wº, WKK WV are learnable projection matrices. The attention output subsequently passed through a standard feed-forward network (FFN) with residual connections and normalization, com- pleting ACDT block. Employing a series of ACDT blocks, A-VIT is configured, progres- sively propagating anatomy-conditionel features throughout the network
3.4 LEARNING OBJECTIVE
To optimize the A-ViT for US image representation, we formulate an SSL strategy that integrates multiple complementary objectives. The proposed SSL framework combines masked image model- ing (MIM) for local structural recovery, an adversarial objective to preserve high feghhfreeuency speckle features, and self-distillation objective inspired by DINO for global semantic alignment.
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Masked Image Modeling. Following the scheme of MAE, randomly mask subset of input patches and train the model to reconstruct the missing regions. Let I E R "x denote the input US image, and let M be the binary mask, where M, 0 indicates that the i-th patch is masked. Denote the set of masked indices as f = {i M; = 0}. The reconstruction loss is defined as:
1
CMMM
92
where è represents the generated image. Unlike NIs, US images contain diagnostically critical high- frequency speckle patterns, which are often blurred when training exclusively with an e, ba-ed loss.
Adversarial loss for high-frequency preservation. To enhance the model's ability to recognize speckle details, we incorporate an adversarial loss inspired by generative adversarial networks. A discriminator D(-) is trained jointly to distinguish reconstructed patches from real US patches. The adversarial objective is given by:
(P) adv = R.z log D(x)] E& [log - D()))
C = - E& log D(
(4)
By combining C ady and LMIM, the model not only reconstructs missing regions but also preserves fine-grained speckle patterns crucial for US interpretation.
(G)
(G))
Self-distillation (SD). While adv and LMIM objectives effectively capture local structural cues through patch-level reconstruction, recent studies have demonstrated their limitations in learning discriminative global semantics. To address this, we integrate the DINO framework (Siméoni et al. (025 which introduces a self-distillation scheme across augmented views, thereby enforcing cross-view consistency and enhancing global semantic representation beyond local reconstruction. Let 2, and 2, denote the probability distributions from the student and teacher network output fea- ture, respectively. The SD loss is defined as:
N
CSD
log 2(
(5)
where is the number of output tokens. This objective encourages the student network to align its predictions with the teacher distribution across diverse augmentations, thereby enhancing the global contextual representation of the model.
Final objective and adaptive balancing.
The overall self- self-uuperviee loss is denoted as
V L
L L= (CMIM C(G)),
V VC.()
+
The parameter adaptively balances the contribution of L(G) and LMIM, based on the gradient magnitude.
4 EXPERIMENT
4.1 DOWNSTREAM DATASET
To evaluate the applicability of the proposed A-VIT model across broad spectrum of medical US image recognition tasks, we conducted experiments five distinct classification tasks and semantic segmentation task where US imaging serves as a primary diagnostic modality. Table provides overview of the experimental configurations. Using four publicly available datasets, assessed the efficacy of the proposed A-ViT model for breast cancer, gallbladder tumors, COVID-19 diagnosis, and Echo left ventricle (LV) segmentation. Additionally, for thyroid cancer and cardiac view classification, we constructed relatively large labeled datasets and conducted the corresponding experiments.

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Table 1: Overview of the downstream datasets.
Task
Discase
Tisk
size
Public
Breast (Al-Dhabyani et 2020
Cancer
Classification
655
Yes
Gallblaider Bass et 2222
Tumor Cancer
Classitication
1,255
Yes
Thyroid
Classiliaation
3.428
No
Cardiac
View Cls.
Classification
39,570
No
Cardiac
Echucandiography
Segmentation
10,030
Yes
Lung Bom et al. 202
COVID. 19
Classifiattion
12,108
Yes
For breast cancer classification (Cls) employ the publicly available Breast Ultrasound Images (BUSI) dataset, consisting of 445 malignant and 210 benign tumor cases. The dataset is then split into training (n=505) and testing (n=150) for evaluation. For gallbladder tumor identification, we use the public GBCU dataset, which comprises 823 tumors and 432 normal US images collected from 147 patients. The EchoNet-Dynamic dataset, consists of Echo image and the corresponding LV segmentation masks, is used to assess A- VTT on semantic segmentation (Seg) downstream task. The EchoNet- Dynnmmi dataset is composed of 8,000 train and 2,030 test dataset. To evaluate the perfor- mance of A-VIT on lung ultrasound, we utilized an open-access COVID-19 lung US dataset. Lung videos are collected subject to normal and COVID-19 patients employing low-image quality portable ultrasound devices. The dataset is composed of 9,251 I train sets and 2,857 test cases.
In addition, we propose two medical US datasets. First, a thyroid cancer dataset consisting of 3,428 cases of benign (n=1,496) and malignant B-mode images (n=1,932) is annotated under expert radi- ological and pathological validation. Second, an Echo dataset comprising 15 standard cardiac views is collected and annotated by certified medical professionals. Detailed statistics and annotation pro- tocols are provided in Appendix A.2.
4.2 DOWNSTREAM EVALUATION DETAILS
To evaluate performance on downstream tasks, we follow standard practices in SSL (Caron et al.
(2021); He et al. (2022). Specifically, two representative methods, (1) training a linear classifier on frozen backbone features (linear probing), and (2) fine-tuning the backbone on the downstream
tasks (fine-tuning), are employed. For comparative analysis, the proposed method is benchmarked
against SoTA SSL models in computer vision, MAE (He et al. (2022)). MoCo v3 (He et al. (2020)) ),
(Zhou al. (2021)). SigLIP2 ( et al. (2025)) and DINO (3(Siméomi et al. (2025)
medical US DMAE (Knng et al. (202bb) and USFM (Jiao et al. (0244) and multi-modal medical
image LVM-Med (MH Nguyen et al. (2023) domains. Comparative SSL methods employ the
ViT-B (Dosovitskiy) (2020) with a patch size of 16 as the backbone. A-VIT is configured with the same depth, hidden dimension, and number of attention heads as baselines, thereby maintaining comparable computational cost across models.
For quantitative assessments, we report AUROC, sensitivity, specificity, and accuracy for breast cancer classification, AUROC and Top-1 accuracy for thyroid, gallbladder, and COVID-19 classifi- cation, Dice and mloU for echocardiography segmentation, and Top-1/Tope accuracy for cardiac view classification
EXPERIMENTAL RESULTS
5.1
BREAST CANCER
Table presents the proposed A-ViT comparison with SSL baselines and randomly initialized reference. Overall, A-VIT achieves consistently superior performance under both linear probing and fine-tuning, underscoring its ability to learn discriminative representations for breast cancer classification. Under linear probing, A-VIT attains accuracy of 86.62% surpassing SSL baselines. By contrast, MAE achieves 77.64% accuracy, reflecting the limitation of NI foundation models in US applications, where capturing fine grained,,, high-frequency speckle patterns is essential. With fine-tuning, A- VIT further improves to 93.66%, outperforming the lision-language model SigLIP2 (89.34%) well the US- US-specific foundation model USFM (88.73%).

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Table 2: Quantitative assessment of breast cancer classification under linear probing and fine-tuning.
Probing
Fine-tucing
Method
Accuracy
AUROC [C]]
Scasitivity
Specificity
Accuracy
AUROC [C]]
Sensitivity
Specificity
Supervised
0.8653 0.92)
0.6667
0.9010
MAE
77.64
0.8211 (0.75, 0.89)
0.5747
0.9000
83.09
0,8635 (0.80, 0.92)
0.7636
0.8778
v3
83.09
0.9143 (0.86, 0.96)
0.6875
0.9239
85,91
0.9065 (0.85, 0.95)
0.6897
0,9674
iBOT
$4.50
0,9090 (0.8g 86, 0.95)
0.7632
0:9023
88 $8.02
0.9256 (0.85, 0.96)
0.8347
0.9140
SipLIP2
0.8396 (0.77. 0.90)
0,9351
Dino v3
78.16
0,8382 (0.77, 0.90)
0.6818
0.8446
84,50
0.9172 (0.57, 0,96)
0.6667
0.9326
LVM-Med
82,39
0.8694 81, 0.92)
0.6304
0,9462
84.50
0.9083 (0.86, 0.95)
0,8030
0.8790
DMAE
D.8306 (0.75, 0.90)
0.7188
0.81188
86.61
0,9308 0.97)
0.7238
0.9565
USTM
82.39
0.8927 (0.8) 84, 0.94)
0.7091
0.9022
88.73
0.9376 (0.89, 0.97)
0.8163
0.9344
Proposed
86.62
0.9151 (0.86, 0.96)
0.8333
0.8917
93.66
0,9742 (0.95, 0.99)
0,9455
0.9355
Table 3: Ablation study the effect of loss functions and ACDT in breast cancer classification.
CaD
Caus
fiudy
ACDT
Dataset
Accurncy
AUROC [CT]
Sensirivity
Specificity
Params
Natural Image
0,8635 (0.80.0.92)
0.8778
86M
Ultrasound
89.43 46.34
0.9380 (0.89.0.97)
0,8750
0,9140
86M
Ultrasound
92:25 +2.82
0.9664 (0.93.0.99)
0.8727
0,9570
95M
Ultrasound
92.95
(0.94.0.99)
0,9464
95M
Ultrasund
93.66 +0.71
0.9742 (0.95.0.99)
0.9455
0,9355
95M
Ablation Study. In this section, we analyze the ablation results presented in Table which disen- tangle the contributions of self-supervision objectives and ACDT. The comparison between natural- image pretraining and US-based pretraining under the MIM objective demonstrates the critical role of domain alignment. While -auurel-mmaee pretraining yields an accuracy of 83.09%, US-specific pretraining improves performance substantially to 89.43% suggesting that NI-based representations are insufficient to capture the discriminative cues present in ultrasound. The integration of ACDT leads to 2.79% accuracy improvement, establishing the value anatomy-guided deformable at- tention in refining feature representations. In breast cancer classification, the ability recognize calcifications within breast tumors is particularly important, as these high-frequency patterns of- ten serve critical diagnostic cues. The incorporation of adversarial loss facilitates representation learning of such high-freugg sighffreuenccy components, thereby contributing to the observed improvement in classification accuracy. The incorporation of self-distillation loss further enhances performance, suggesting that the learning objective reinforces global feature learning.
Luno
T-SNE
Ultrasound
Proposed
MAE
Proposed
Rbb Shadow
Breast tumer
Anatomy MAE
Utrrasou nd Imoos
Proposed Gland
Ultrazou Imoo
Pepposed
Figure 3: Feature principal component analysis (PCA) and t-SNE analysis of MAE and A- A-V
Feature PCA and t-SNE Analysis Fig. 3 demonstrates principal component analysis (PCA) of features extracted by SSL models. comparing the MAE and the A- ViT across multiple US domains. The visualization qualitatively confirms that the proposed A-VIT yields more discriminative and anatomically faithful feature representations. In the breast domain, A-ViT more precisely delineates lesion morphology, enabling clearer representation of shape-related cues essential for malignancy assessment. In lung imaging. the model effectively captures the pleural line and rib shadow, which are fundamental landmarks for identifying pulmonary abnormalities. For cardiac US, A- A-ViT pro- vides distinct representation of the LV region, highlighting its ability encode clinically relevant cardiac structures. Fig. 3(e) introduces t-SNE analysis features extracted by MAE and A-ViT across different anatomies. A-VIT yields distinct and separable clusters, thereby demonstrating
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Table 4: Quantitative assessments of the networks across multiple US downstream tasks.
Cardacc Seg.
Cardicc view Cls.
Thyrid cuncer Cls.
COVID-19 Cls.
Gallbladder Tumar Cls.
Dice
mloU
Top-1
Top-3
Acc.
AUROC [CI)
Acc.
AUROC [CT)
Acc.
AUROC [CI)
MAE
89.21
81.06
89.07
99.19
82.50
0.9110 (0.89, 0.93)
80.74
0.9346 (0.92, 0.95)
K3.39
0.9105 (0.88, 0.94)
MoCo v3
89.80
82.01
91.08
99.02
83.50
0.9112 (.93)
82.22
0.9286 (0.92, 0.91)
54.47
0.9237 (0.89, 0.95)
SPgLIP2
90.60
90.97
99.11
(0.91. 0.95)
78.05
(0.88, 0.90)
$4.11
0.9123 (0.88, 0.94)
Dino x3
90.76
83.35
$9.93
99.12
86.24
0,9428 (0.93, 0.96)
$6.94
0,9465 (0.93, 0.96)
$4.84
0:9189 (0.89, 0.95)
IBOT
89,45
81.37
90.59
99.13
83.68
0.931 (0.92.
81.55
0.9494 (0.94, 0.96)
$5.19
0.9213
LVM-Med
89.41
81.30
98.72
83.42
0.9001 (0.89. 0.93)
85.40
0.9359 (0.92. 0.95)
80.15
0,8700 (0.5 82, 0.91)
DMAR
90.44
83.02
90.50
99.20
84.72
0.9210 (0.90.
85.99
0,9059 (0.89, 0.92)
$4.83
0.9019 (0.87. 0.94)
USFM
0.9301 (0.91, 0.95)
0.9475 (0.94, 0.96)
0.9347 (0.90. 0.96)
Proposed
92.16
85.67
91.80
99.22
$7.07
0.9475 0,96)
91.44
0.9714 (0.97, 0.98)
89.89
0.9511 (0.93, 0.97)
Top-1 Accuracy by dateset size
Top-3 Accuracy by dataset size
HAE
DMAE
Tae-
MAG
DANS
Deraze Sun
Denaed Bun
Figure 4: Cardiac view classification by the number of dataset. Top-1 and Top-3 accuracy are presented.
enhanced anatomical discriminability. The results demonstrate that the V-VIT captures anatomy- specific US features with higher fidelity, thereby contributing stronger generalizability and im- proved accuracy in downstream diagnostic tasks.
5.2 CARDIAC SEGMENTATION
The effectiveness of the proposed A-VIT model is evaluated on the Echo semantic segmentation to demonstrate its capability in dense prediction tasks. A-ViT is evaluated using Dice score and mean intersection over union (mloU) metrics. To adapt the ViT for dense prediction, decoder layer of UPerNet (Xiao et al. (2018)) is employed and fine-tune the entire network. Quantitative results, summarized in Table 4, indicate that A-VIT outperforms the baseline models, achieving enhanced Dice score and mloU, thus demonstrating efficacy in dense prediction scenarios.
5.3 CARDIAC VIEW CLASSIFICATION
We perform multi-class classification experiment across 15 standard Echo views. Given the rela- tively low inter-view variation in Echo images compared to NIs, Echo video classification requires a more detailed level of feature extraction capture the precise distinctions between views. Such dif- ferences demand highly refined learned representations. Under fine-tuning evaluation, the proposed A-VIT achieves Top-1 accuracy of 91. 91.80% demonstrating a substantial improvement over com- peting SSL baselines. The result emphasizes A-ViT's ability to learn detailed, anatomically relevant features that are crucial for distinguishing between Echo views.
Ablation Study: effect of dataset size. To investigate the impact of training dataset size on car- diac view classification, we progressively decreased the dataset size down to 1% its original volume. As shown in Fig 4, the A-ViT demonstrates less performance degradation than the baseline approaches under such limited data conditions. A comparison of top- accuracy reveals that even when trained only 1% of the data, approximately 0.4K samples, the proposed model outperforms the best performironnng baseline by a significant margin. The experiment demonstrates the robustness and efficiency of A-V in learning high-quality representations from substantially smaller datasets.

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5.4 EXTENDED EVALUATION OF MEDICAL ULTRASOUND IMAGE ANALYSIS
Thyroid cancer classification. Table.4 presents the performance of the finetuned models in be- nign and malignant thyroid cancer classification. The proposed method shows the highest accuracy (87.07%) and AUROC (0.9475), demonstrating potential for clinical application of cancerous thy- roid tumor diagnosis.
COVID-19 identification. The A-VIT is evaluated for COVID-19 identification, a critical diag- nostic application in medical US. The method achieves a top-1 accuracy of 91 9..4%, outperforming comparative approaches. The experiments show the efficacy of the A- A-V in utilizing lung-specifes features, demonstrating potential for clinical application of COVID-19 19 identification through US.
Gallbladder tumor identification. A comprchensive experiment is conducted to assess the perfor- mance of SSL methods in gallbladder tumor classification. Gallbladder US images are characterized by their convex structure, which presents attributes markedly distinct from those of NIs. The pro- posed method demonstrates superior performance, achieving the highest accuracy (89.89%) and AUROC (0.9511). The evaluation demonstrates that the A-ViT exhibits robust feature discrimina- tion for gallbladder tumor classification.
CONCLUSION
In this paper, we introduce Anatomy-aware Representation Learning, a novel framework designed to address the unique challenges of medical US imaging, such as speckle noise, low color variability, and anscofy- attributes. By incorporating the A-ViT and leveraging a newly configured large-scale US dataset, ARL enables generalizable feature extraction adaptive to specific anatomical contexts. Experimental results demonstrate the effectiveness of ARL over state-of-the-art baselines in major medical US applications including breast cancer, thyroid cancer, gallbladder tumor, cardiac view classification, and COVID-19 identification, achieving significant improvements in accuracy and AUROC. The experiments demonstrate the potential of the ARL. reliable and efficient solution for computer-aider diagnostics, offering the foundation for broader integration of Al-driven methods into clinical workflows.
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A
APPENDIX. DETAILS OF THE PUBLIC DATASET EMPLOYED FOR THE STUDY
Table 5: Summary of ultrasound datasets used for representation learning and downstream applica-
tions.
Dataset Name
Modality
Anatomy
Usage
BUS-BRA
Ultrasound
Breast
Representation learning
BUS-UCLM
Ultrasound
Breast
Representation learning
BUV
Ultrasound
Breast
Representation learning
DDTI
Ultrasound
Thyroid
Representation learning
Thyroid Ultrasound Cine-clip
Ultrasound
Thyroid
Representation learning
EchoNet-LVH
Ultrasound
Echocardiography
Representation learning
CAMUS
Ultrasound
Echocardiography
Representation learning
HMC-QU
Ultrasound
Echocardiography
Representation learning
Fatty liver dataset
Ultrasound
Liver
Representation learning
FALLMUD
Ultrasound
MSK
Representation learning
Ovarian dataset
Ultrasound
Ovarian
Representation learning
BUSI
Ultrasound
Breast
Downstream application
RchoNet-Dynanne
Ultrasound
Echocardiography
Downstream application
POCUS
Ultrasound
Lung
Downstream application Downstream application
GBCU
Ultrasound
Gallbladder
In this study, we introduce a novel large-scale medical ultrasound dataset, along with detailed de- scriptions of the thyroid cancer and cardiac classification datasets proposed for evaluation.
A.1 LARGE-SCALE MEDICAL ULTRASOUND DATASET
The proposed large-scale medical ultrasound dataset comprises 5.2 million images, integrating data from 11 publicly available ultrasound datasets (Gómez- et al. (2024); Lin al. (2022); Vallez et al. 20 Pedraza et al. (2015b); AIMI; Ouyang et al. (201 9); Duffy et al. (2022); Leclere et al.
(2019) Degerli al. (2024); ;; Byra et al. (018);; Cunningham al. (2018); van den Heuvel et al.
2018 Yap et al. (2017) ()) and additional collections from 12 medical institutions in the United States, 2 in South Korea, and in India. Data acquisition procedures were conducted under Institutional
Review Board (IRB) approval and in strict compliance with the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), thereby ensuring both ethical and legal data handling. The dataset encompasses 16 distinct categories of medical ultrasound imaging. For publicly available datasets, anatomical classes are explicitly defined at the dataset level, and for newly curated data, anatomical labels are assigned using exam types and metadata. The anatomical classes consist of Abdominal, Aorta, Bladder, Breast, Cardiac, Doppler, Esophageal, Gallbladder, Kidney, Liver, Lung, Muscle, Oncology, Soft tissue, Testicle, Thyroid, and Other, thereby comprehensively representing diverse anatomical regions and clinical applications. To the best of our knowledge, this constitutes the largest publicly reported medical ultrasound dataset to date, exceeding existing datasets by an order of magnitude in size. A detailed summary of the datasets incorporated in this large-scale collection provided in the table.
A.2 THYROID ULTRASOUND TUMOR DATASET
The thyroid cancer evaluation dataset is also presented in this study, specifically configured for evaluating classification performance. The thyroid dataset includes a total of 3,428 thyroid tumor B-mode ultrasound images, each annotated for benign (n=1,496) or malignant (=1,932) status. The annotations are conducted with three certified sonographers, with an expert radiologist confirming the annotations to establish ground truth. Ground truth labeling for malignant tumors is determined based on pathological results from tissue biopsies confirming malignancy. For benign tumors, cases benignity through follow-up examinations
labeled either through aatholoyy-confimen period of at least years, during which consistent radiological findings indicated benign features. The thyroid cancer dataset is divided into training and testing subsets, with 2,662 images used for
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training and 766 images reserved for testing. The proposed dataset is, to the best of our knowledge, the largest publicly reported thyroid ultrasound dataset available to date
A.3 ECHOCARDIOGRAPHY 15 VIEW DATASET
The echocardiography classification evaluation dataset comprises 39,570 echocardiography im- ages collected for classification tasks. The images are annotated by three experienced sonog- raphers with extensive echocardiography expertise. The dataset includes standard echocardiog- raphy views classified into 15 categories: Apical 2-Chamber (A2C), Apical 3-Chamber (A3C), Apical 4-Chamber (A4C), Parasternll Long-Axis (PLAX),, Parasternal Short-Axis the Apex (PSAXAAPEX: Parasternal Short-Axis at the Aortic Valve Level (PSAX_AV), Parasternal Short- Axis the Mitral Valve Level (PSAX_MV) Parasternal Short-Axis at the Papillary Muscle Level (PSAX PM), Right Ventricular Apical 4-Chamber (RVA4C), Right Ventricular Inflow View (RVIV), Right Ventricular Outflow View (RVOV), Subcostal 4- 4-Chamber View (SC4V), Subcostal Long-Axis View (SCLA), Suprasternal Notch View (SSN). The dataset is split into training and testing subsets, with 33,520 images used for training and 6,050 images reserved for testing.
B APPENDIX. IMPLEMENTATION DETAILS
Linear probing. For linear probing on breast cancer classification, employed a classification head of linear classifier processing the global average pooled output feature of vision transformer. The vision transformer encoder weight is frozen, and weight of the classifier head is optimized through stochastic gradient descent with a momentum of 0.9. During training, we employed hor- izontal flip and random resize crop augmentation with image height and width of 224 and 224, respectively.
Fine-tuning. The fine-tuning downstream evaluation is conducted for breast, thyroid, and gallblad- der cancer classification, COVID-19 identification, and cardiac view classification. In contrast to linear probing, which trains only shallow classification head on frozen features, fine-tuning op- timizes every parameter of the Vision Transformer encoder together with the classification head. AdamW optimizer is employed with base learning rate of 0.001, with a cosine decay schedule with 5-epoch warm-up up. 31 = 0.9, 32 = 0.999.
Semantic segmentation. Echocardiography left ventricle blood-pool segmentation is experimented for the assessment of the anatomy-awar vision transformer for dense prediction tasks. UPerNet (iaoo et al. (2018) configuration is employed. The network is trained using the AdamW optimizer with learning rate of 0,0001, with a learning objective of minimizing the unweighted sum between cross-entropy loss and Dice loss as proposed in Ma et al. (2024).
C APPENDIX. EFFECT OF PRETRAINING DATASET SCALE
To assess the impact of pretraining dataset scale representation learning quality, varied the number of ultrasound images used for self-supervised pretraining from 5.2M down to 52K and evaluated A ViT breast cancer classification -tneitu The results in Table that both accuracy and AUROC degrade as the dataset size decreases, highlighting the importance of large- scale and diverse ultrasound data for effective and generalizable representation learning.
Table 6: Effect of pretraining dataset scale on breast cancer classification (fine-tuning).
Pretraining Dataset Size
Accuracy
AUROC
5.2M (100%)
93.66
0.9742
520K (10%)
90.84
0,9581
52K (1%)
87.23
0.9353
D APPENDIX. NATURAL- AND ULTRASOUND-IMAGE PRETRAINING
We further compared sulf-supe methods pretrained on natural-iman natural-image datasets and on the pro- posed ultrasound dataset. Table reports breast cancer classification results (fine-tuning). Across
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MAE, DINO, and iBOT, srtrassuund-sppccinn pretraining consistently improves accuracy and AU- ROC over ImageNet pretraining, confirming the benefit of modality- pretraining. The pro- posed A- V-Vi,, trained on ultrasound data with ACDT, achieves the best overall performance.
Table 7: Breast cancer classification performance under natural-image vs ultrasound-image pretrain- ing.
Mothod
Pretraining Data
Accuracy
AUROC [C]]
Scusitivity
Spocificity
MAE
Natural Image Dataset
83,09
0.8635 (038,3,, 0.9193)
0.76
0.88
MAE
Proposed Ultrasound Datasct
89.43
0.9380 8915, 0.9724)
0.88
0.91
DINO
Natural Image Dataset
84.50
0.9172 (0.8652, 0.9605)
0.67
0.93
DINO
Proposed Ultrasound Datasct
90.14
0.9682 (0.9430, 0.9900)
0.89
IROT
Natural Image Dataset
88.02
0.9256 (0. 8805, 0.9644)
0.83
0.91
IBOT
Proposed Ultrasound Datasel
0.9662 (0.94000, 0.9859)
0.87
A-VIT'
Proposed Ultrasound Dutaset
93.66
0.9742 (0.9481, 0.9930)
0.95
0.94
E APPENDIX. COMPARISON OF CONDITIONING MECHANISMS
In order to assess the proposed ACDT, we compared alternative conditioning mechanisms for incor- porating anatomy information into the transformer backbone, including cross-attention, FiLM, and LoRA. Table g summarizes breast cancer classification performance (fine-tuning). The proposed ACDT achieves the highest accuracy and AUROC, indicating that mantomy-condiiioned deformable attention is particularly effective for capturing anatomy -ssoomyy feature distributions in ultrasound.
Table 8: Comparison of conditioning mechanisms on breast cancer classification (fine-tuning).
Conditioning Scheme
Accuracy
AUROC
Cros-atlention
90.84
0,9578
FiLM
90.14
0.9624
LoRA
92.25
0,9692
Proposed ACDT
93.66
0.9742
F APPENDIX. ADAPTIVE Loss WEIGHTING
We evaluated the impact of the adaptive loss-weighting scheme that balances the masked-image modeling and adversarial objectives. MSE-based losses predominantly capture low-frequency struc- ture, whereas adversarial losses better preserve high-frequency content. Fixed weighting can lead to imbalanced optimization of the complementary image features. Adaptive weighting prevents dom- ination by single loss term and stabilizes representation learning. Table presents breast cancer classification performance under constant and adaptive weighting. Adaptive weighting yields higher accuracy and AUROC, supporting its effectiveness in stabilizing optimization and jointly modeling low- and high-frequency image characteristics.
Table 9: Effect of loss weighting strategy on breast cancer classification (fine-tuning..
Loss Weightng Strategy
Accuracy
AUROC
Constant weighting
92.95
0.9701
Adaptive weighting
93.66
0.9742
G APPENDIX. MASKED-IMAGE RECONSTRUCTION ANALYSIS
To assess how well the learned representations capture ultrasound-specffc characteristics, we con- ducted masked-iman -mnge reconstruction experiment. Masked B-mode images encoded using (i) a standard ViT pretrained as a MAE on natural images and (ii) the proposed A- A-ViT encoder, followed by shared MAE decoder.
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Published as a conference paper at ICLR 2026
Figure presents side-by-side reconstructions for both encoders under identical masking patterns. Reconstructions obtained from A-VIT more faithfully preserve uih-frequency speckle patterns and fine-gratd tine-grained structural details, whereas the cetturallmageeprettee ViT produces overly smoothed outputs.
(a) Breast Anatomy
Ultrasound Image
Masked Image
MAE
Proposed
(b) Thyroid Anatomy
Ultrasound Image
Masked Image
MAE
Proposed
UMG terees
(c) Cardiac Anatomy
Ultrasound Image
Masked Image
MAE
Proposed
(d) Lung Anatomy
Ultrasound Image
Masked Image
MAE
Proposed
Figure 5: Masked-image reconstruction comparison between standard MAE and the proposed A- ViT with MAE decoder. A-ViT reconstructions preserve ultrasound-speen -specifi speckle patterns and fine structural details.
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Published as a conference paper at ICLR 2026
H APPENDIX. COMPUTATIONAL COST AND INFERENCE SPEED
To evaluate the clinical deployability of A-VIT, we analyze its computational complexity and in- ference speed in comparison with standard Vision Transformer (ViT-B/16) backbone. FLOPs computed for single 224 x 224 B-mode ultrasound image, and inference speed is measured on single NVIDIA RTX 4090 GPU.
As summarized in Table I0 A-ViT introduces only a modest increase in parameter count and FLOPs relative to the standard ViT, while providing substantially improved anatomy-aware representations in downstream tasks.
Table 10: Computational cost and inference speed comparison between standard Vision Trans- former and the proposed A-VIT.
Model
Parameters
FLOPPs
Inference Speed (ms / image)
Vision Transformer (VTT-Br) B/16/
86M
17.58G
12.1
A-VIT (proposed)
95M
17,66G
16.6
I APPENDIX. ETHICS STATEMENT
All ultrasound data utilized in this study were fully anonymized in compliance with the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). The studies were conducted under the institutional review board approval of Seoul National University Bundang Hospital (SNUBH).
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Reviews:
[{"review_id": "qJq9PAIAIO", "reviewer": "Reviewer_BKQr", "summary": "This paper proposes an anatomy-aware representation learning framework (ARL) for medical ultrasound (US) imaging, based on a large-scale US dataset and a novel Anatomy-aware Vision Transformer (A-ViT). The method incorporates anatomical context via a deformable transformer and combines multiple self-supervised objectives to improve feature learning. Extensive experiments across multiple downstream tasks demonstrate improved performance over existing self-supervised learning baselines.", "questions": "1.The authors are advised to check the title of the paper: “ANATOMY-AWARE REPRESENTATION LEARNING FOR MEDICAL ULTRASOUND.” There appears to be an extra symbol (a period at the end).\n\n2.Why was the deformable attention mechanism chosen over other spatial-aware transformers? Were alternatives considered?\n\n3.How does the model perform when the anatomical label is noisy or misassigned?\n\n4.Is the performance gain mainly from the proposed architecture or the large-scale dataset? An ablation on dataset scale would be helpful.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1.The paper introduces a large-scale, multi-source US dataset, which is a valuable contribution to the community.\n\n2.The proposed A-ViT model effectively integrates anatomical information and shows consistent improvements across diverse US tasks.\n\n3.The combination of MIM, adversarial loss, and self-distillation is well-motivated and empirically validated.\n\n4.Comprehensive evaluation on multiple organs and tasks (classification and segmentation) strengthens the claim of generalizability.", "weaknesses": "1.The motivation for choosing certain design elements (e.g., deformable attention, specific loss weighting) could be better justified.\n\n2.The comparison to other anatomy-aware or medical-specific transformers is limited.\n[1]Anatomy-Aware Contrastive RepresentationLearning for Fetal Ultrasound\n[2]Anatomy-Aware Self-Supervised Learning for Aligned Multi-Modal Medical Data\n[3]SELF-SUPERVISED REPRESENTATION LEARNING FOR ULTRASOUND VIDEO\n\n3.The computational cost and inference speed of A-ViT are not discussed, which may limit practical deployment.\n\n4.Some baseline results (e.g., DINO v3) are strong, and the margin of improvement is not always substantial.\n\n5.The main text lacks detailed statistical information about the dataset, such as organ, image size, depth, and classes.", "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": "8wAzxEnmgV", "reviewer": "Reviewer_h8d3", "summary": "This paper proposes Anatomy-aware Representation Learning (ARL), a self-supervised representation learning framework tailored specifically for medical ultrasound imaging. The authors identify key challenges in ultrasound diagnostics, including qualitative variability, reliance on expert knowledge, the unique texture/structural properties of the images, and the scarcity of large-scale datasets. The ARL framework is intended to address these issues. The work is evaluated by fine-tuning a Vision Transformer encoder and classification head on a diverse set of medical downstream tasks, including breast, thyroid, and gallbladder cancer classification, COVID-19 identification, and cardiac view classification, as well as a dense prediction task (echocardiography left ventricle blood-pool segmentation) using a UPerNet configuration.", "questions": "1.Please fully describe the proposed Anatomy-aware Representation Learning (ARL) framework. What are the specific self-supervised tasks or loss functions that compel the model to learn \"anatomy-aware\" features? How do these tasks specifically leverage or model the unique texture and structural attributes of ultrasound images better than standard methods?\n2.Please provide the full set of quantitative results (e.g., AUROC, F1-Score, Dice Similarity Coefficient) for all mentioned downstream tasks (classification and segmentation), and critically, compare your method against strong baselines such as ImageNet pre-trained models and non-anatomy-aware self-supervised learning methods applied to ultrasound data.\n3.Did the authors perform an ablation study to justify the anatomical-aware component? Showing results without the 'anatomy-aware' loss/mechanism would be critical evidence for the necessity and effectiveness of the proposed novelty.\n4.How are anatomical labels obtained for the 5.2M pretraining images? Are they derived from metadata, manual annotation, or automated prediction? If the latter, could label noise degrade representation quality?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.The integration of anatomical conditioning into deformable attention for US is a novel and well-motivated architectural contribution. Unlike prior SSL methods that treat all images uniformly, ARL explicitly conditions feature extraction on anatomical context.\n2.The 5.2M-image dataset is a significant contribution, and the ablation studies (Table 3, Fig. 4) convincingly isolate the impact of each component (ACDT, adversarial loss, etc.).\n3.The paper is well-structured and clearly written, with intuitive figures (Fig. 1, 2, 3) and logical flow.\n4.Medical US is an underexplored modality in SSL, and ARL provides a strong foundation for future work. The dataset alone will likely become a community resource.", "weaknesses": "1.The central weakness is the lack of a clear description of the Anatomy-aware Representation Learning (ARL) mechanism itself. While the goal is anatomy-aware learning, the specific self-supervised loss function or task design that enforces this awareness is not described, making it impossible to fully assess the work's technical depth or novelty.\n2.The submission provides an extensive experimental plan but no quantitative results (e.g., AUC, F1, Dice Score). Without empirical evidence, the claim of technical soundness and significance remains unverified. It is impossible to determine if the proposed ARL method actually advances the state-of-the-art or even works as intended.\n3.The paper claims to address the unique texture and structural attributes of ultrasound images. However, without detailing how ARL achieves this (i.e., the mechanism of anatomy-awareness), its true originality over existing self-supervised methods (like SimCLR, MAE, etc.) applied to medical imaging cannot be confirmed.", "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": "EM3XMPy9uS", "reviewer": "Reviewer_cf7P", "summary": "This paper proposes Anatomy-Aware Representation Learning (ARL), a self-supervised framework for ultrasound (US) imaging that integrates anatomical context into representation learning. The authors propose Anatomy-aware Vision Transformer (A-ViT), which incorporates an Anatomy-Conditioned Deformable Transformer (ACDT) to extract features according to the organ being analyzed. The authors evaluate ARL across multiple downstream tasks, including breast, thyroid, and gallbladder cancer classification, cardiac view classification, cardiac segmentation, and COVID-19 diagnosis. Experiments show that ARL consistently outperforms state-of-the-art (SoTA) self-supervised methods.", "questions": "1. Are there evidence that the proposed model addresses speckle noise and low color variation challenges in ultrasound imaging.\n\n2. How is the proposed model differed, comparing to existing models for other modalities? Is there any architectural choice that is uniquely designed for ultrasound or directly tied to ultrasound physics?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper introduces a foundation model for ultrasound imaging, trained on 5.2M images, covering 16 anatomical categories.\n\nThe model is tested on six downstream tasks across both classification and segmentation. \n\nQuantitative results demonstrate consistent gains over state-of-the-art baselines.", "weaknesses": "The authors emphasizes speckle noise and low color variation as major challenges unique to ultrasound imaging. However, the proposed A-ViT primarily introduces anatomy-conditioned deformable attention, which addresses anatomical variability rather than these low-level texture or color issues. The authors introduces an adversarial term to preserve high-frequency content. However, the paper lacks direct evidence that speckle-related distortions are mitigated or that low-color variation are effectively handled. \n\nAnatomy-aware conditioning was previously proposed in MRI/CT segmentation & registration. Deformable attention exists in general vision. MAE + distillation hybrid ideas has also been previously proposed. Therefore the paper doesn’t introduce a new model, but rather a new instantiation tailored to ultrasound images. However, there is limited explicit explanations on how each architectural choice is uniquely designed for ultrasound or directly tied to ultrasound physics. Most of the mechanisms introduced in A-ViT are general and could apply to other modalities. As a result, the authors should better explain the novelty by explicate how A-ViT differs from existing models for other modalities. \n\nIn Tables 2 and 3, the proposed model does not have the highest specificity in some cases and the highest specificity values are not bolded correctly", "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)