Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Authors: Divyanshu Mishra
Mohammadreza Salehi
Pramit Saha
Olga Patey
Aris T Papageorghiou
Yuki M Asano
Alison Noble
Venue: NeurIPS 2025 poster ()
Keywords: Medical Imaging
Ultrasound
Self-Supervised Video Represenational Learning
Self Supervised Learning
Echocardiography
Fetal
Video Understanding
Primary area: machine_learning_for_sciences
TL;DR: DISCOVR is a self-supervised framework for echocardiography video representation learning that integrates spatial and temporal modeling, achieving strong generalization in anomaly detection, segmentation, and LVEF prediction.

Abstract: Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex temporal dynamics, and the current lack of domain-specific pre-trained models. Existing SSL approaches such as contrastive, masked modeling, and clustering-based methods struggle with high intersample similarity, sensitivity to low PSNR inputs common in ultrasound, or aggressive augmentations that distort clinically relevant features. 

We present DISCOVR (Distilled Image Supervision for Cross Modal Video Representation), a self-supervised dual-branch framework for cardiac ultrasound video representation learning. DISCOVR combines a clustering-based video encoder that models temporal dynamics with an online image encoder that extracts fine-grained spatial semantics. These branches are connected through a semantic cluster distillation loss that transfers anatomical knowledge from the evolving image encoder to the video encoder, enabling temporally coherent representations enriched with fine-grained semantic understanding. 

Evaluated on six echocardiography datasets spanning fetal, pediatric, and adult populations, DISCOVR outperforms both specialized video anomaly detection methods and state-of-the-art video-SSL baselines in zero-shot and linear probing setups, achieving superior segmentation transfer and strong downstream performance on clinically relevant tasks such as LVEF prediction.

**Code available at:** [https://github.com/mdivyanshu97/DISCOVR](https://github.com/mdivyanshu97/DISCOVR)

Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Divyanshu Mishral
Mohammadreza Salehi
Pramit Saha'
Olga Patey²
Aris T. Papageorghiou
Yuki M. Asano
J. Alison Noble'
Department of Engineering Science, University of Oxford
Nuffield Department of Women's 's Reproductive Health, University of Oxford Fundamental AT Lab, University of Technology Nuremberg
University of Amsterdam divyanshu mishraGeng. ox wx.acccui uk
Abstract
Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex temporal dynam- ics, and the current lack of domain -speiin-peccfii pre-trained models. Existing SSL. approaches such as contrastive, masked modeling, and clustering-basee methods struggle with high intersample similarity, sensitivity low PSNR inputs common in ultrasound, or aggressive augmentations that distort clinically relevant features. We present DISCOVR (Distilled Image Supervision for Cross Modal Video Repre- sentation), self- supersudee dual branch framework for cardiac ultrasound video representation learning. DISCOVR combines a clustering bastering-based video encoder that models temporal dynamics with an online image encoder that extracts fine-grained spatial semantics. These branches are connected through a semantic cluster distil- lation loss that transfers anatomical knowledge from the evolving image encoder to the video encoder, enabling temporally coherent representations enriched with fine- ine-grained semantic spanning fetal, pediatric, and adult populations, DISCOVR outperforms both spe- cialized video anomaly detection methods and state-of-the-n video-SSL baselines in zero-shot and linear probing achiesia superior segmentation transfer and strong downstream performance clinically relevant tasks such LVEF prediction. Code available at: http://hwww..oommm//d///u///////
six echocardiography datasets
Introduction
Modeling dynamic content in video data presents significant challenges due to complex spatio- temporal relationships, high redundancy between frames, and the need to capture both short- and long- range temporal dependencies 32). Echocardiography (heart or cardiac ultrasound) exemplifies
these video understanding challenges 23 22). With high frame rates (30-80 80 fps) (2I), complex anatomical motion. and variability in image appearance caused by speckle, shadowing artifacts, and ultrasound probe variability (18). automated echocardiography analysis requires sophisticated temporal modeling approaches. The information density in these videos is high, where features critical for diagnosis may appear subtle variations in wall motion, valve function, blood flow patterns that manifest only when viewed dynamically across multiple frames. Moreover, the appearance of the heart can change drastically across different cardiac views, patient populations, and imaging equipment. Developing robust video SSL models for comprehensive video understanding in echocardiography faces additional obstacles due data limitations. Expert annotations costly, intersinte and if based on real-world hospital data often incomplete, capturing only specific
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Natural vs Ultrasound Video
Normal Adult Heart
c995
Abnormal Adult Heart
COMS
100
Prarnes 206
Viire
CSB
114
350
Figure 1: Figure (left) compares two fine- fineggriied videos: natural scene of a person baking (left) and an adult fetal heart ultrasound (right). The frame-level cosine similarity matrix, computed using pretrained VideoMAF model, shows that ultrasound frames are highly similar (mean=0.99), with only minor local variations. This highlights the difficulty in distinguishing individual frames such medical videos. Figure (right) compares normal and abnormal adult echocardiograms that appear nearly identical. However, on close inspection, is revealed that the abnormal heart shows severe biventricular systolic dysfunction and dilated, globular left ventricle, underscoring the subtlety of cardiac defects and the need for fine-grain ine-grained structural analysis.
aspects of the rich information contained in these videos. This scarcity of labeled data motivates SSL approaches that can leverage abundant unlabeled echocardiograms for model development 332 24 6
Several SSL frameworks have been proposed for learning meaningful video representations, each with particular limitations in the echocardiography context. Masked video modeling methods [35] 10 tend to focus on reconstructing low-level image features like textures or edges, limiting their ability to capture high-level semantic information critical for clinical interpretation. This is especially problematic for ultrasound, which inherently exhibits a low signal-to-non ratio (SNR), making approaches that rely low-level pixel representations ineffective. Contrastive learning methods [3 27 struggle due to high inter-sample similarity and limited effective augmentations, making difficult construct informative positive and negative pairs, often leading to representation collapse. Clustering-basee based SSL methods have demonstrated strong semantic learning through self self-distillation but rely heavily aggressive augmentations that risk disrupting essential anatomical details required for fine-grained understanding.
To address these limitations, we propose DISCOVR (Distilled Image Supervision for Cross-Modal Video Representation), a dual branch SSL framework tailored for echocardiography that jointly captures temporal dynamics and fine-grained semantic structure. The video encoder is trained to model temporal features using clustering based objective applied to masked vidco tokens, while an online image encoder separately learns spatially rich and anatomically meaningful representations from masked image views. To bridge the gap between spatial and temporal learning, we introduce a semantic cluster distillation loss that transfers knowledge from the evolving image encoder to the video encoder through semantic cluster alignment. This enables the video encoder to embed fine-grained semantic detail into its temporally coherent representations, without relying on pretrained models or heavy augmentations.
We extensively evaluate DISCOVR on six echocardiography datasets that span fetal, pediatric, and adult populations, covering anomaly detection, classification (linear probing and zero-shot transfer), and segmentation tasks. DISCOVR consistently outperforms prior self-supervised and anomaly detection methods. It achieves average Fl improvement of 3,4% for anomaly detection, 2.4% gain in linear probing, and a 1.5% increase in balanced accuracy under zero shot evaluation. For segmentation, DISCOVR delivers 3.1% 1% relative improvement in Dice score (from 81.9 to 84.4), despite using a simple segmentation head compared to more complex baseline architectures. These results demonstrate that integrating spatial semantics with temporal dynamics through cross distillation yields robust and generalizable cardiac ultrasound video representations. Overall, our contibutions
follows:
We develop an SSL method that jointly models temporal dynamics and spatial semantics by integrating video self-distillation with an evolving semantic image encoder, without labels, pretrained models, or augmentations.

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Time
Semantic Cluster Distillation
vid Essi
L SCD
Video Encoder
Decoder
H
(s v 9i
SD patohes
Time
Temporal Features
Time
Time
Image Encoder
LSED
Sinkhorn
Sinkhorn Targets
Clustering
20 patches
Time
img
Online Fine-grained Spatial
Gradients
Figure 2: Overview of the DISCOVR framework. An input video is tokenized into 3D patches for the video branch and per-frame 2D patches for the image branch. Both encoders perform masked self-distillation. Masked video tokens reconstructed by the video decoder, and dense semantic features are extracted from the image encoder. The LSCD loss then aligns these outputs, distilling fine-grained spatial semanties into the video representation produce rich spatio-temporal features.
We introduce novel online semantic distillation loss that continually transfers anatomical knowledge from the evolving image encoder the video encoder, enriching its temporal representations with fine-grained spatial semantics to better capture clinically relevant spatio-temporal patterns echocardiography.
DISCOVR is, to knowledge, the most comprehensive self- superviee video represen- tation model for echocardiography to date. Trained solely on normal videos, it models healthy heart dynamics and detects pathology deviations, eliminating the need for labeled abnormal cases. Evaluated across six datasets spanning fetal, pediatric, and adult cohorts, DISCOVR demonstrates strong generalization in zero-shot classification, linear probing, anomaly detection, and segmentation, and achieves state-of-the-ar performance on down- stream cardiac function estimation (LVEF prediction), making versatile backbone for ultrasound analysis.
2
Related Work
Self-supervised learning (SSL) aims to learn feature extractors directly from raw data by solving an intrinsic task using supervision signals derived from the data itself, eliminating the need for manual labels. Early image-based SSL relied on handcrafted pretext tasks such as solving jigsaw puzzles (26). predicting rotations (TI. or colorizing grayscale inputs 45 Recent methods have shifted towards instance discrimination via contrastive learning 42
To understand how these ideas extend to video and medical domains, review the most relevant self-supervised methods in both areas, highlighting shared limitations and how DISCOVR R addresses them.
13 5
Video Self-Supervieed Learning. Extending SSL to video introduces additional temporal com- plexity, inspiring tasks such as frame order prediction [25 43), spatio-temporal jigsaws [6), and playback pace prediction
38 Recently, masked video modeling has become the dominant approach: VidcoMAE [35] reconstructs raw pixels from masked tubelets using a VIT backbonc.
3
predicts optical flow enhance temporal modeling, and motion- masking [9] highlights dynamic regions. SIGMA [33] replaces pixel-level targets with Sinkhorn reggulaaree cluster assignments, encouraging learning of semantic features. Yet, these approaches often rely on frozen teachers, handcrafted objectives, or sensitive clustering parameters. DISCOVR addresses these issues by introducing video eelf-distillation with evolving semantic guidance from image encoder, aligning fine- grained spatial and temporal features to produce coherent, high-level video representations, without external supervision, handcrafted tasks, modality-speciee assumptions.
MGMAE [14]
Self-Supervised Pretraining for Medical Videos. Given the limited availability of annotated data, several works have adapted vidco SSL techniques medical domains. Jiao et al. (15) explored

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frame order and transformation prediction for fetal ultrasound. EchoFlow [3] generated synthetic echocardiograms via adversarial VAEs and latent flow. Although effective in context, these methods inherit key limitations from natural video SSL,, including reliance on frozen teachers, hand-crafted objectives, and sensitive clustering parameters. In addition, they adopt design choices tailored to natural images, such as short clip lengths and the lack of mechanisms for capturing fine-gri fine-grained spatial cues, both of which are inadequate for clinical video analysis, where longer temporal context and detailed spatial reasoning introduces dynamic semantic guidance from an evolving image encoder, enabling the video backbone to learn rich, fine-gru grained apatio-temporal representations without reliance pretrained models or handcrafted supervision.
critical. In contrast, DISCOVR uses long (64-frame) clips and
Methodology
The modelling of echocardiography video-based tasks poses unique challenges, as models must simultaneously detect fine-grained anatomical details, such as subtle septal defects, and accurately track how these features evolve throughout the cardiac eycle to reliably identify anomalies. We propose unified self-supervised framework addressing these aspects without relying on labelled data external pretrained models. Our method integrates three complementary techniques: (I) video self-distillation to capture global cardiac motion, (2) online spatial guidance to learn fine- grained structural information, and (3) semantic cluster distillation (SCD) loss to transfer fine-grained semantic knowledge from the evolving image encoder to the video model.
3.1 Video Deff-Distillation
To capture how cardiac structures evolve throughout the cardiac cycle, it is essential to learn spatio- temporal representations from echocardiography videos. We propose a video-level self-distillation framework based on student-teacher architecture with Vision Transformer (ViT))based encoders (Fig. ( that models temporal dynamics and improves understanding of global heart motion. Given a video input v, partition it into non-overlapping 3D space-time patches (tube tokens), and prepend a learnable class (CLS) token, resulting in a sequence zo, Z1,..., IN, where zi is the CLS token.
The teacher encoder Eg, processes the complete, unmasked video to produce global representation, whereas the student encoder Eg, processes multiple masked variants U.I...., UMar each applying distinct random space-time masks to enforce inference of missing content.
Both encoders output global video representation via the CL.S token:
Eo, (v)
(m) = Eo, (l)..)
The teacher parameters are updated using exponential moving average (EMA) of the student parameters:
0g + A0; + x)6,, X 10.
(2)
These CLS embeddings are subsequently mapped through linear projection heads characterized by learnable weight matrices W, (teacher) and W. (student). The resulting embeddings are transformed into probability distributions via temperature-scalee scaled softmax operations:
Wi2
W (m))
Pt = softmax
((m) softmax
(3)
Ti
where 7, and T, temperature parameters for the teacher and student, respectively. We align these probability distributions using the cross-entropy loss:
M
Luid
Σ M m==1
H(P,, H(m),
(4)
where H denotes cross-entropy. This approach encourages the student to match the teacher's global representation of cardiac motion, despite observing only incomplete views of the video. Through video-level self-distillation, the student learns to recover the evolving dynamics of anatomical landmarks, capturing coherent motion patterns and structural features relevant to global heart function throughout the cardiac cycle.

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3.2 Fine-Grained Online Spatial Guidance
Although video self-distillation promotes temporal consistency and global abstraction, it tends to overlook fine- -eee-gerand spatial features, particularly those critical to clinical interpretation in echocardio- graphy. Echocardiography imaging captures the dynamics and uppearance of anatomically complex structures, where capturing subtle spatial details, such as mitral valve leaflet motion, septal wall thickness, or endocardial border definition, is crucial. To address this, we introduce a two-part strategy for enriching spatial detail and semantic structure in video representations:
a). Masked Image Self- Dont-iittllationn An online image encoder is trained learn spatially rich features from partially masked images, enabling the extraction of fine-grained semantic concepts. b). Semantic Cluster Distillation (SCD). cross-modal clustering objective aligns reconstructed video tokens with spatial image features, encouraging the video model to organize its representation space around semantically meaningful structures.
3.2.1 Masked Image Self-Distillation
To learn fine-grained semantic features, we train an image encoder In in parallel with the video encoder. Each video is decomposed into individual frames (zz), which processed independently. For each frame x, the teacher image encoder To, receives the full-resolution image, while the student encoder Is, given N randomly masked variants {I.M, J Each output is projected using distinct learnable heads We (teacher) and Wa (student), followed by softmax normalization:
W W.Io, (KM)))
W.To. (x)
p(() softmax
P: softmax
(5)
Ta
Tt
where T, and T. are temperature parameters. The loss function encourages the student to match the teacher's predictions across all masked views:
Cimg
H(),,
(6)
with H(:.)) denoting the cross-entropy. This training objective promotes the emergence of spa- tially grounded representations that encode fine-grained clinical concepts such as fetal heart valves, ventricular anatomy, and septal delineation that may be underrepresented in purely temporal learning.
3.2.2 Semantic Cluster Distillation (SCD)
While Masked Image Self-Distillation enables the image encoder to learn spatially grounded repre- sentations that capture fine-grained clinical concepts, does not transfer this knowledge to the video encoder. As result, the spatial and temporal representations remain disjoint. To bridge this gap, introduce Semantic Cluster Distillation (SCD), a cross- modal objective that distills semantic structure from the image encoder, guiding the video encoder incorporate fine- ine-grained spatial detail into its token representations.
Given masked video input, the student video encoder F.o, processes the visible tokens to produce latent representations, which are then passed to a decoder & that reconstructs token-level features 2, IR Bx where B is the batch size, N is the number of masked tokens, and D) is the feature dimension. In parallel, the corresponding video frames are processed by the image encoder Io, producing spatial features 2; E IR BxNxD These image features are detached from the gradient flow and serve as semantic targets. Both sets of features are projected onto a shared set learnable prototypes P K resulting in similarity scores:
P.P
S; PP
where 7 a temperature scaling parameter and K the number of prototypes. The resulting scores transformed into Sinkhom soft cluster targets using the MankhornKnoop algorithm:
qe = Sinkhorn (s.), qi = Sinnhhomss.)..
(8)
The SCD loss symmetrically aligns the two modalities by minimizing the cross-entropy between their soft cluster assignments:
Lsco CE(s,, stopgrad (q:)) CE(s;, (q.))
(9)
5

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GT:
Table Comparison of video anomaly detection methods on three echocardiography datasets. Our method consistently outperforms SOTA ap- proaches, demonstrating improved effectiveness in identifying cardiac abnormalities across diverse patient populations.
Abnormal
Ours
Pred:
MVD Nermal
Dataset
Model
Balanced Ace.
AUC
MNAD
52.25
52.08
53.15
EchoNet-
MemAE
49.22
46.33
49.69
Dynamic
C2FPI.
57.36
57.35
59.00
Figure 3: Zero-Shot classification compar-
Ours
63.20
61.45
67.06
ison: (Top) The sweep from four-chamber to three-vessel view reveals smaller left-sided structures (LV and Ao) versus right-sided (RV and PA), consistent with coarctation of the aorta. (Middle) DISCOVR correctly identi- fies the abnormality, focusing the ventri- cles in the four-chamber view and the Ao and PA in the vessel view. (Bottom)A backbone pretrained with MVD, in contrast, misclassi- fies the video as normal.
MNAD
52.34
52,18
54.05
MemAR
47.65
32.10
44.68
RVENET
C2FPL
47.88
47.86
46.30
56.23
53.88
57.42
MNAD
47.86
47.85
47.31
Rcho
MemAE
47.28
47.28
47.23
Pediatric-
C2FPL
$1.39
51.31
50.68
LVH
Ours
55.63
54.63
57.23
where gradients are propagated only through the video model and the prototype matrix P, while the image encoder is updated solely via its own self-distillation loss. This guides the video encoder to anchor its token representations to the spatially grounded clusters discovered by the image encoder, thereby distilling -ee-graaded anatomical detail into temporal feature space. Semantic Cluster Distillation thus embeds spatial semantics within temporal features, yielding spatio-temporal representations that capture anatomically relevant detail in echocardiography videos.
Experiments and Results
Datasets. We use five ultrasound video datasets across fetal, pediatric, and adult populations. Two private fetal heart datasets, FetalEchol and FetalEcho2, were each collected from different hospital partners in the UK, comprising 10 10-second transverse, cephalad sweeps capturing five standard cardiac views (Situs, 4CH, LVOT, 3VV, 3VT). FetalEchol includes 8273/414/311 and FetalEcho2 includes 4154/320/305 videos for training/validaatoonn validation/testing. For adult and pediatric cchocardiography. 3 public datasets: EchoNet Dynamic (apical 4CH adult; 7378/1326/13 (366))36) (28), EchoPediatric LVH (parasternal long-axis pediatric; 7837/1592/151 595/199999))) [8], and RVENet (right ventricular pediatric/adult; 2516/487/573) [20]. Videos for adult and pediatric populations are labeled as normal or abnormal based ejection fraction (EF), with abnormal defined EF 45% or EF 75% (7]. Fetal videos are labeled as normal or abnormal based on expert evaluation by two fetal cardiologists(+10 years of experience). For the downstream segmentation task, utilize the CAMUS 19 dataset.
Evaluation. All baseline models use official implementations, with videos sampled in 64-frame clips stride of 3. We adopt space-time tube embeddings from VideoMAB (35), treating each 2 x 16 x 16 cube as a token with 90% masking ratio. All models use a ViT base backbone with consistent configurations. We evaluate representations using zero-shot classification and linear probing. Zero-shot evaluation uses weighted kNN classifier [42] 4 on frozen features, with k selected based on validation balanced accuracy. Linear probing trains linear classifier for 30 epochs on frozen backbone using a labeled validation set. During inference, each test video is divided into 64-frame clips and classified independently; video is labeled abnormal if any clip predicted abnormal. For segmentation evaluation, we add a linear layer followed by Conv2D upsampling blocks to generate pixel-level masks while keeping the backbone frozen.
Baselines. We compare DISCOVR with SOTA video SSL methods SIGMA 33 MGMAE [1]]
MVD (39), VideoMAE (35), and RAD-DINO (30, covering masked modeling, clustering, and dense
feature learning. For anomaly detection, we include SOTA methods MNAD (29], MemAB [12]

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Table 2: Linear probing classification sults on five echocardiography datasets spanning fetal, adult, and pediatric popula- tions. Our method achieves SOTA results, outperforming prior video SSL baselines and generalizing effectively across diverse clini- cal cohorts.
Table 3: Zero-shot evaluation across five echocardiography datasets covering fetal, adult, and pediatric populations. Our method consistently outperforms existing video SSL baselines, demonstrating robust generalization across diverse clinical populations.
Dataset
Population
Model
Aee
Bal. Arc.
F1
Dataset
Model
Acc
Bal. Acc.
F1
55.35
ViasMMA
VideoMAA
60.19
60.01
59.82
Feul- Echo
SIGMA
$4.37
54.91
51.90
Fetal- Fcho
MGMAE
59.55
59.40
59.30
MOMAB
MVD
59.87
00.84
60.20
61.03
59.15
Oun
02.46
62:79
61.79
Ours
65.70
65.52
65.39
RAD
54,10
51.46
50.62
Fetal- Echo
MGMAE
VideoMAA
60.98
56.39
60.49
53.12
60.43
51 $1.60
Feul-
Fetal
SIGMA
MCMAR
Vi3aMAE
$0.49
55.43
$1.90
48.01
56.71
SIGMA
56.06
55.81
MVD
Ours
65.25
63,53
63.59
99.67
57.18
VideoMAE
71.04
70.86
70.85
RAD-DINO
VideoMAE
57.16
99.43
57.91
59.63
55.07
$9.34
Echonet- Dynamic
SIGMA
MGMAE
75.57
61.84
75.48
61.81
75.50
61.81
Echonct- Dynamic
Adal
MCMAR
SIGMA
53,47
$1.21
$4.45
52.23
46.13
49.04
Ours
77.68
77.61
77.63
MVD
60.11
60.34
$7.56
62,59
63.20
61.45
Fcho
VideoMAE
60.87
60,94
60.71
RAD-DINO
52.27
Podiatric-
MGMAE
SIGMA
Ours
58.42
62.81
57,27
61.64
57.24
61.66
Bcho Peciomic LVH
Pediatric
MGMAB
SIGMA
MVD
VidoeMAE
49.56
26.61
47.55
51.91
49.55
53.98
48.91
45.45
46.30
48.46
50.47
VidcoMAE
60.03
60.31
59.70
Dur
$4.65
55.63
$4.63
MOMAE
RAD-DINO
55.67
55.65
55.65
RVENET
SIGMA
59.51
59.25
58.98
Vide-MAR
SIGMA
$2.96
53.18
MVD
55.12
Chrra
53.67
53.88
and C2FPL [2] which rely solely on spatial-temporal learning without external modules like object detectors, pose estimators, or optical flow, often tailored to natural images.
4.1 Comparison with Video Anomaly Detection Methods
Table [ compares the anomaly detection performance of DISCOVR with several state-of-the art approaches. DISCOVR achieves the highest F1 score for all datasets (61.45% for EchoNet Dynamic, 53.88% / for RVENET, and 54.63% for EchoPediatric LVH) as well as the highest balanced accuracy (63.20%, 56.23%, and 55.63%, respectively), substantially outperforming C2FPL, MemAE, and MNAD for all reported metrics. C2FPL relies on a multi-stage pseudo-labeling process to enhance anomaly discrimination, while both MemAE and MNAD incorporate sophisticated memory mechanisms and feature regularization in their inference pipelines. These methods employ targeted, anomaly-specific inference strategies and complex architectures.
outperfor
In contrast, DISCOVR builds on a simple self-supervised learning framework that jointly learns spatial and temporal features, utilizing only straightforward zero shot kNN classifier at inference. DISCOVR R not only achieves state-of -f-te-of-thheane art scores, including the highest AUCs of 67. 76 on EchoNet Dynamic, 57.42 on RVENET, and 57.23 on EchoPediatric LVH, but also demonstrates that richer spatio-temporal representations learned via simple SSL can offer more effective and efficient anomaly detection than more sophisticaed anomaly detection techniques without reliance on specialized or resource intensive modules.
Linear Probing. Table 2 shows that DISCOVR achieves the highest balanced accuracy and Fl score in linear probing for anomaly detection across all echocardiography datasets. For example, on Echonet Dynamic, DISCOVR attains an Fl of 77.63 compared 75.50 for SIGMA, and on FetalEcho 2, achieves 63.59 versus 60.43 for MGMAE. These improvements are consistent across fetal, pediatric, and adult cohorts. While VideoMAE and MGMAE rely on high masking ratios and pixel-level reconstruction, their representations often miss subtle anatomical landmarks and temporally distributed abnormalities, reflecting a lack deeper semantic abstraction. Clustering- based approaches such SIGMA can capture some temporal variation but lack explicit semantic guidance, limiting their ability to identify clinically relevant landmarks. contrast, DISCOVR leverages semantic supervision from the image encoder through online distillation, combined with
7

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Inpur
VideoMAE
MOMAE
SIGRM
Ours
85.0
84.4
Dice Score (%)
81.6
80.0
78.7
75.9
75.0
74.7
70.0
(PYOL)
BATLII
Figure 4: Barplot comparing the segmentation performance across different models. Our pro- posed DISCOVR approach achieves the highest Dice score of 0.844, outperforming both special- ized segmentation architectures (Deepl .bb V3, UNET) and other self-supervised methods.
Segmentation comparison on the CAMUS dataset for left ventricular endo- cardium (LV Endo), left ventricular epicardium (LV Epi), and left atrium (LA). Our method pro- duces accurate and consistent masks, achieving higher Dice scores (bottom right) than baseline methods.
Figure 5:
temporal modeling in the video branch. This enables DISCOVR to capture fine-grained spatial features and their evolution over time, resulting in representations that are both robust and clinically meaningful for anomaly detection in cardiac ultrasound.
Zero-Shot. Table 3 that DISCOVR achieves the highest balanced accuracy and Fl score for zero shot classification across all echocardiography datasets. For example, on Echonet Dynamic, DISCOVR reaches an Fl of 61.45 compared 57.56 for the best baseline, and on FetalEcho achieves 61.79 versus 60.64 for MGMAE. These improvements are consistent across fetal, pediatric, and adult cardiac cohorts. This stronger performance reflects DISCOVR's 's ability to integrate semantic features captured by the image encoder with temporal dynamics modeled by the video branch, explicitly aligned through SCD loss during self-supervised training. Pixel reconstruction models such as VideoMAE and MGMAE focus primarily on low-level appearance and texture, and clustering approaches like SIGMA, while using temporal clips, lack explicit semantic guidance. Image-based baselines like RAD-DINO do not leverage temporal information, while methods such as MVD that rely on external pretrained teachers may be less adaptable to the clinical and domain- specific challenges of ultrasound video. DISCOVR's capabilities qualitative example of Fig. 3. where detects subtle cardiac structures and correctly classifies a challenging fetal video abnormal, while MVD fails to capture these cues and predicts normal outcome. This underscores how DISCOVR's features are sufficiently fine-graing me-ggranee to enable accurate zero shot anomaly detection, even without task-specifie tuning.
further highlighted in the
4.2 Segmentation Evaluation
We evaluate the effectiveness of DISCOVR representations for downstream cardiac segmentation using the CAMUS dataset [19). As shown in Fig DISCOVR achieves the highest Dice score (0.844), outperforming specialized segmentation architectures such as UNet and DeepLabV3 (0.816 and 0.819, respectively, both with BYOL pretraining). When compared using a simple linear+upsampling head on frozen backbone, DISCOVR also surpasses other SSL-based video models, including VideoMAE (0.747). MGMAE (0.767), and SIGMA (0.759). Fig. 5 highlights these advantages: DISCOVR produces consistently accurate and well-aligned segmentation masks for LV Endo, LV Epi, and especially the left atrium. For the challenging left atrium segmentation (blue mask), MGMAE misses the structure entirely (Dice = 0.01), while SIGMA and VideoMAE also perform poorly (Dice 0.30 and 0.56). DISCOVR, in comparison, achieves 0.90, demonstrating superior ability to segment subtle structures and delineate boundaries due to its fine-grained feature learning.
4.3 LVEF Prediction
We evaluate the effectiveness of DISCOVR representations downstream cardiac function esti- mation using the EchoNet-Dynamic ejection fraction dataset (28) As shown in Table DISCOVR achieves the lowest Mean Absolute Error (MAE) of 7.79 under the standard linear probing setup, outperforming other self-supervised baselines such as VideoMAE (8,02) and MGMAE (8.88). When

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fine-tuning only the last three encoder blocks, DISCOVR further reduces the MAE to 6.32, demon- strating the strength of its learned representations even with limited adaptation. In comparison, the fully supervised FichNet-DDynmmic model 28 is trained end to end with all parameters updated. Under an ejection fraction-only setup without segmentation labels, DISCOVR surpasses these fully supervised baselines, including MC3 with an MAE of 6.59, the base SchoNet--Dnammi model with 7.35, and R3D with 7.63. The full Sehhoeet-Dynamii architecture achieves an MAE of 4.05 using a large multi-task design with 71.1 million parameters co-trained on 20,060 manual segmentation trac- ings. These results show that DISCOVR, through self-supervised pretraining and partial fine-tuning, learns powerful cardiac representations that rival exceed fully supervised models trained end to end.
Table 4: LVEF prediction results on the EchoNet-Dynamic dataset. Our self-supervised method is compared against other SSL methods and fully-supervised baselines from (28).
Model
MAE
RMSE 1
EF Labels
Seg. Labels
Self-Supervised (Linear Probing)
VideoMAE
8.02
11.16
MGMAE
8.88
12.47
DISCOVR (Ours)
7.79
10.89
Sele-Suuervised (Fine-tuning)
DISCOVR (finetune last 3 blocks)
6.32
8.62
Fully-Superiie Baselines [1] trained only with EF Data
MC3 (All frames)
6.59
9.39
EchoNet-Dynamic (EF, All frames)
7.35
9.53
R3D (All frames)
7.63
9.75
DISCOVR (finetune last 3 blocks,64 frames)
6.32
8.62
FchoNee-Dynamii (Full model)
4.05
5.30
5
Ablation Study
In this section, we ablate the key components of the training objective in our model, DISCOVR. All experiments are conducted on the Echonet Dynamic dataset and evaluated using the k-nearest neighbor (kNN) protocol. This setup allows us to assess the discriminative quality of the learned representations in fully frozen setting without additional fine-tuning.
Effect of Loss Components. We evaluate the effect of two core loss components used in DISCOVR:
(i) the video self-distillation component (Cvid) and (ii) the semantic cluster distillation component with online image guidance (CsCD). Table [5a reports the performance of these losses individually and in combination in zero-shot settings. Using only Cyid yields modest performance (F1 48.23%), as it primarily captures global temporal structure via CLS tokens but lacks guidance for fine-grained semantics. Introducing ESCD leads to a substantial improvement (F1 61.45%, Balanced Accuracy 63.20%), as the evolving image-based based semantic clusters enrich the temporal features learned by the video model and encourage focus more ine-grained, spatially grounded information. For more detailed ablation, refer to supplementary section 1.1.4.
Effect of Backbone Size. We investigate how transformer backbone size impacts DISCOVR's representation quality. We evaluate ViT-Small and ViT-Base variants, each paired with matching DINO image encoders, on the Echonet Dynamic dataset using kNN evaluation (Table 5b).. ViT-Base achieves superior performance (F1=61.45) 145% balanced accuracy=63.c (3.22a compared to ViT-Small (F1=57.52%, balanced accuracy=59.4449 The smaller model's 's reasonable performance indicates DISCOVR learns meaningful representations even with limited capacity.
Effect of Number of Frames. In this ablation, we evaluate how the number of frames sampled from each video clip affects the representational quality learned by our model. We experiment with three temporal lengths: 16, 32, and 64 frames. All other training settings are kept constant, and the results are reported Table 5d We observe a clear upward trend in performance with increasing frame count. Using 16 frames results in an Fl score of 55.68%, %,, which improves to 57.4 37.45% with 32 frames. The best performance achieved with frames, yielding an Fl score of 61.4 61.45% and balanced accuracy of 63.20%. These results support the intuition that ultrasound, being a temporally dense and

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Table 5: Ablation studies reported with Balanced Accuracy, Precision, and Fl score.
(a) Effect of loss terms
(b) Backbone size
cuid
LsCD
Bal, Acc.
Precision
F1
Backbone
Bal. Ace.
Precision
F1
x
$2.27
$3.16 65.35
48.23 61.45
ViT-Small
59.
61.03
$7.52 61.45
/ 63.20
ViT-Base
63.20
65.35
(c) Masking ratio
(d) Number of frames
(%)
Acc.
Precision
Frames
Bal. Acc.
Precision
50
35,60
56.90
$2.98
16
57.89
59.45
55.68
75
56.25
57.58
53.85
32
59.54
61.36
57.45
90
63.20
65.35
61.45
64
63.20
65.35
61.45
dynamic modality, benefits from longer clips. More frames provide richer temporal context, enabling the model capture fine-grained spatial and temporal motion patterns across the cardiac cycle.
Effect Masking Ratio. Table 5c shows a steady improvement in performance as masking ratio increases, with Fl-score rising from 22.98% (50%) to 61 .45% (90%). Higher masking forces both the video encoder and the semantic image guidance branch to infer more from sparse visual cues, encouraging the model to focus the most salient and non-redundant features. This promotes the learning of richer representations that better capture subtle and fine-grained spatio-temporal patterns, resulting in improved anomaly detection performance.
Computational Cost and Scalability. We report the computational cost, our method Table The table shows both training and inference statistics, showing GPU memory usage and Fi-score for each method on Sehoaei-Dynamic, for batch size of 1, 64 frames, and spatial size 112 112. During training, DISCOVR uses slightly more GPU memory (10.5GB) compared to prior methods (between 9.0 and 9.5GB) but achieves notable +6.38% improvement in F1-score over the closest competitor. At inference, all methods, including DISCOVR, use identical ViViT-like encoders, resulting nearly the same GPU memory footprint and FLOPS. This demonstrates that our method's performance improvements come with minimal extra training cost and no penalty for inference efficiency.
Table Training and inference GPU memory, FLOPS, and Fl-score on EchoNet-Dynamn batch size 1, 16 frames, 112 x 112 resolution.
Model
Train Mem (GB)
F1-score
Infer Mem (GB)
Infer: FLOPS
MGMAE 14
46.13
1.153
101.85
VidcoMAR
9.0
55.07
1.153
101.85
SIGMA 33
9.2
49.04
1.153
101.85
Video-distillation
9.5
48.23
1.153
101.85
DISCOVR (Ours)
10.5
61.45
1.153
101.85
6 Conclusion
We introduce DISCOVR, a self-supervised model for learning video representations in echocar- diography across diverse patient populations. Our approach combines masked video modeling, temporal self-distillation, and online spatial supervision, unified by a Semantic Cluster Distillation (SCD) objective that aligns video and image features through cross-modal clustering, without re- lying on labeled anomalies or pretrained models. Extensively evaluated on six echocardiography datasets spanning fetal, pediatric, and adult populations, DISCOVR consistently outperforms previous self-supervised and anomaly detection methods for multiple tasks, including anomaly detection, classification (zero-shot and linear probing), and segmentation. DISCOVR's task-agnostic design and applicability diverse patient groups establish it as a strong foundation for screening cardiac conditions and developing assistive tools for echocardiography.
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Acknowledgments
We acknowledge financial support from InnoHK-funded Hong Kong Centre for Cerebro- cardiovascular Health Engineering (COCHE), UKRI grant EP/X040186/1, UK EPSRC grant EP/T028572/1 (VisualAD), UK EPSRC Doctoral Training Partnership award and, UKRI AIRR Early Access Project No. ANON-BYYG-VXAC-2
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Appendix
A Dataset Distribution
This section presents the dataset distributions for five echocardiography video datasets: Fe- talEcho1 (Fig(6), FetalEcho2 (Fig EchoNet-Dynamil (Fig EchoNet-Pediatrie (Fig()). and RVENET (Fig For each dataset, the bar chart displays the number of unique samples in the training, validation, and test sets. The accompanying pie charts illustrate the class distributions (Normal Abnormal) within the validation and test sets.
Fetal-Echol Split Distribution
Val Class Distribution
Test Class Distribution
Nomal Abnomal
Nornal Abnormal
7000
Number of Videos
3000
4000
202
212
151
166
2000
Train
Voil
Test
Figure 6: Dataset Distribution for Fetal-Echol dataset
Split
Val Class Distribution
Test Class Distribution
4000
Number of Videos
3000
2500
150
132
170
1500-
205
Train
Test
Split
Figure 7: Dataset Distribution for Fetal-Echo2 dataset
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Echodynamic Dataset Distribution
Val Class Distribution
Test Class Distribution
7376
7000
hormal Annomal
Normal Abnormal
8000
Number of videos
5000
4000
642
684
2000
2000
1826
1326
1000
Train
Voil
Test
Figure 8: Dataset Distribution for Echo- Stho-Dynamic dataset
Split Distribution
Val Class Distribution
Test Class Distribution
8000
Norml Annomal
Normwl Abnornal
Number of Videos
6000
5000
679
3000
1992
1000
Splitt
Figure 9: Dataset Distribution for Echo-Pediatric dataset
RVENET Dataset Split Distribution
Val Class Distribution
Test Class Distribution
2500
2000
Number of Videos
258
229
294
1000
500
487
Taan
Vail Split
Test
Figure 10: Dataset distribution for RVENET dataset
B Additional Results:
B.1
Full Finetuning
B.1.1
Evaluation Setup
We follow the same evaluation procedure as described in the experiments section in the main paper, but fine-tune the entire backbone along with the linear layer. All other evaluation settings remain
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unchanged. Results are reported on the Echonet Dynamic dataset to assess end-to-end supervised performance.
B.1.2 Evaluation Result:
Under full fine-tuning, as shown in Table all models experience a drop in performance compared to their linear probing results, reflecting overfitting due to the limited labeled validation data. Despite this, DISCOVR achieves the highest F1 score of 70.44%, outperforming MGMAE (63.99%), SIGMA (61.46%), and VideoMAE (57.31%) %).. DISCOVR's structured representation learning, through tempo- ral distillation and cross-modal clustering, appears to provide more robust and adaptable features, enabling to generalize better even when fully fine-tuned on small dataset.
Table 7: Table showing the full-finett fill-finetuning result of DISCOVR compared to other baselines on the -Dyaaic Dataset
Model (%)
Accuracy
Balanced Acc.
Precision
F1-Score Recall
VideoMAE
57.62
57.94
58.27
57.94
57.31
SIGMA
61.69
62.00
62.41
62.00
61.46
MGMAE
65.99
66.08
66.10
66.08
65.99
Ours
70.51
70.42
70.50
70.42
70.44
B.1.3 Generalisation to Other Modalities.
To test whether DISCOVR generalizes beyond echocardiography, we evaluated its transfer per- formance on two distinct medical image benchmarks: the Breast Ultrasound Images dataset [ (cancer detection across 600 patients) and DermMNIST [44] (skin lesion classification). Both breast ultrasound and echocardiography require the detection of small, irregular regions of altered tissue, such hypoechoic tumors in the breast or localized wall motion abnormalities in the heart, making the ability to identify subtle structural changes in one domain directly applicable to the other. Sim- ilarly, DermMNIST demands fine- fine-grained visual discrimination between morphologically similar skin lesions. For both benchmarks, we froze the DISCOVR encoder and trained a linear classifier, comparing performance directly across methods.
As shown Table our method demonstrates strong generalization across both tasks. On the Breast Ultrasound dataset, DISCOVR improves balanced accuracy by 2.01% over VideoMAE, 19.83% over SIGMA, and 12.01% over MGMAE. For DermMNIST, DISCOVR achieves an accuracy of 71.68% outperforming VideoMAE by 2.85% SIGMA by 3.00% and MGMAE by 3.85%. These results demonstrate strong generalization to multiple medical image analysis tasks beyond echocardiography. Further, assess generalization natural video data, pretrained and evaluated all models the Kinetics 400 action recognition benchmark, using a zero-shot protocol where KNN classification with K 20 was applied to features using 64 frames from the frozen video backbone. As shown in Table 9 DISCOVR achieves the highest Top-1 accuracy at 22.3%, outperforming MVD by 3.6%, MME 3.2%, and VideoMAE by 1.6%, while also requiring the fewest pretraining epochs. These results highlight that DISCOVR not only excels at medical video tasks but also learns generalizable representations efficiently for large-scale natural video datasets.
Table 8: Linear Probing results on the Breast Ultrasound dataset and DermMNIST. For Breast Ultrasound, we report Balanced Accuracy (Bal. Acc.) and Fl; for DermMNIST, we report overall Accuracy (Acc.).
Breast Ultrasound
DermMNIST
Method
Balanced Accuracy
F1 Score
Accuracy
VideoMAR
61.75
64.45
68.83
SIGMA
43.93
42.21
68.68
MGMAE
51.75
52.34
67.83
DISCOVR (Ours)
63.76
65.44
71.68
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Table Bero-Shot KNN classification performance on Kinetics-400
Model
Epochs
Top-1 Accuracy (%)
MVD
39
1600
18.7
MME 34
800
19.1
VideoMAE
655
800
20.7
DISCOVR (Ours)
400
22.30
B.1.4 Loss function Ablation detailed.
To rigorously evaluate each component, we have added baselines using only masked image or only video self-distillation. Indeed, we find that the settings perform suboptimally, shown in Table 1, confirming that spatial or temporal cues alone are insufficient for strong representation learning. In contrast, combining both with the SCD loss, which explicitly distills fine-grained semantic structure from the image branch into the video backbone, achieves the best results. This supports our intuition that SCD is crucial for aligning spatial semantics with temporal dynamics, enabling more robust and clinically meaningful video representations.
Table 10: Effect of the different loss terms on classification performance (Balanced Accuracy, Precision, and FI).
List
Cimg
LSCD
Bal. Acc.
Precision
52.27
53.16
48.23
/
53.66
55.22
49.43
63.20
65.35
61.45
C Implementation Details
All models are implemented in PyTorch 2.6 and trained on RTX 8000 GPUs (48 GB) with a batch size of using the Adam W optimizer. Videos processed as 64-frame clips sampled stride of and resized to 112 x 112.
For both video and image self-distillation, we use student-teacher setup where the teacher processes the full input and the student observes N randomly masked views. The teacher network
updated via exponential moving average (EMA) of the student with momentum À 0.996. A
fixed temperature т, 0.1 is used for the student, while the teacher temperature T, is linearly warmed
from 0.04 to 0.07 over the first 30 epochs. Semantic Cluster Distillation (SCD) uses K 3000
learnable prototypes, with similarity scores computed via temperature-scalee dot products (T 0.1) and cluster assignments generated using the Sinkhorn -noopp algorithm (10 iterations,
0.05).
Models are trained for 400 epochs with a learning rate 1.5 x 10 weight decay of 0.05, and 40 warmup epochs.
D Broader Impact and Limitations
In this work, we introduce DISCOVR, a novel self-supervised model for echocardiography video understanding across fetal, pediatric, and adult populations. Trained without labeled abnormal cases, DISCOVR learns rich spatiotemporal representations and enables zero-shot inference. One key application of DISCOVR in the early screening of heart diseases, where it can assist clinicians by flagging potential anomalies in echocardiography videos. This has significant clinical relevance, as congenital heart defects affect approximately in 100 newborns, with up to 50% missed during prenatal screening [6 17 37], and cardiovascular diseases remain the leading global cause of death [40]. By reducing reliance on large, labeled datasets, DISCOVR offers a scalable and accessible solution, particularly for deployment in low-resource settings.
While DISCOVR shows strong potential, its current scope focused specifically on echocardiography; and it has not yet been evaluated other imaging modalities. The model trained and tested on
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five datasets collected from distinct clinical sites, each with its own imaging protocols. devices, and patient cohorts. As a result, the demographic and geographic diversity of the data may be limited. Further validation is needed to assess the model's generalizability across broader clinical settings, populations, and imaging systems.
Natural vs Ultrasound Video
Abnormal Fetal Heart Video
Frame=1
Frame=20
Frame-70
Frame=90
Normal Fetal Heart Video
Framie+
Frame=20
Frame:90
Figure 11: Figure (left) compares two fine- ane-grained videos: a natural scene of person baking (left) and an adult heart ultrasound (right). The frame-level cosine similarity matrix, computed using pretrained VideoMAE model, shows that ultrasound frames are highly similar (mean=0.99), with only minor local variations. This highlights the difficulty in distinguishing individual frames such medical videos. Figure (right) compares normal and abnormal fetal echocardiograms, which appear almost identical despite one being abnormal. This illustrates the inherent difficulty of distinguishing subtle cardiac abnormalities in fetal imaging.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: In this paper, develop self-supervised video foundational model for echocardiography and show its performance across fetal, pediatric and adult population across various downstream tasks. Our novelty lies in the DISCOVR framework, which combines a clustering based video encoder for temporal dynamics with a DINO-insping image encoder for spatial semantics, unified through novel online semantic distillation loss that transfers anatomical knowledge from the evolving image encoder to the video encoder without relying on labels external pretrained models.
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It is fine include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
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Justification: Our model is focused on echocardiography, and we have made it clear in the introduuction/abstract and conclusion. Further, we have added a broader impact and limitations section in the appendix to discuss our assumptions and limitations of the current work.
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In our paper, have theoretical results.
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(d)
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Question:
Answer: [No]
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Justification: Reporting classical confidence intervals or p-values would require training each configuration multiple times estimate variance, an approach that impractical for video self-supervised learning (SSL. methods, which typically demand substantial computational resources and incur high environmental costs. Instead, we assess robustness by evaluating our model architecture on five distinct echocardiography datasets covering fetal, pediatric, and adult populations, Across all settings, our method consistently outperforms strong baselines by large margin, demonstrating reliable generalization without requiring repeated runs.
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We mention the societal impact of our work which has potential to support experts in detecting heart diseases across diverse patient populations and enhance clinical workflows through various downstream applications in the abstract, introduction, and con- clusion sections. We have also added separate Broad Impact and Limitations section in the appendix.
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Answer: [Yes]
Justification: We have cited all sources whose data or code we have utilized. Descriptions of each dataset provided below:
(a) Fetal-Echol and Fetal-Echo2: These are private datasets collected as part of private project. Ethics approval has been obtained, and data were anonymised before model development. We will provide the ethics approval number and any other required documentation upon publication, in compliance with the double-blind review policy. (b) EchoNet-Dynamic and EchoNet-Pediatric Both are publicly available datasets released by Stanford University for non-commercial research use under Stanford University Research Use Agreement, which we adhere to. For more information, see: httts:// wet.hb..i//yaaaae...... pediatric/
https://echonet.. wwacchonnt.ggthhu.... -it.gg.tthubi. io/
(c) RVENet: This dataset is available for non-commercial research use under a Research Use Agreement with Semmelweis University, which we comply with. For more information, see: htttp:://vveet:: gi github. 10 lo/dataset/
(d) CAMUS: This dataset is publicly available for research of the original publication, which we have provided. For more information, see: https://vvir creatis, insa- insa-nnonnnn.e.. fr wetr/hhallengg/eeeee camps/ hwtp//databbssseee html
and requires citation
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Reviews:
[{"review_id": "GS22O0P2mr", "reviewer": "Reviewer_6qum", "summary": "This work introduces DISCOVR, a self-supervised learning framework for echocardiography video representation. It combines masked video self-distillation, masked image self-distillation, and image-to-video semantic cluster distillation to capture both temporal dynamics and fine-grained spatial semantics. Experiments on six echocardiography datasets demonstrate that it outperforms existing SSL methods and anomaly detection methods in tasks like anomaly detection, classification, and segmentation.", "questions": "See weaknesses.", "limitations": "No.\n\nThe authors briefly mention in the appendix that their study focuses on echocardiography, but the discussion of limitations and potential negative societal impacts remains insufficient. A more thorough analysis—particularly one based on failure cases—would help to identify and communicate the boundaries of the method’s applicability. For example, understanding when and why the model fails could reveal biases, generalization limitations, or risks of misuse in clinical settings. I encourage the authors to include a more detailed discussion of these aspects to strengthen the ethical and practical reflection of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is clearly written and well-structured.\n2. Experimental results are positive and fit the paper.\n\nWeaknesses:\n1. In the ablation study, to rigorously evaluate the contribution of the video self-distillation component, a baseline that includes only the masked image self-distillation (without the video component) should be provided. This comparison is necessary to isolate and quantify the effect of video self-distillation.\n2. Left Ventricular Ejection Fraction (LVEF) is a critical clinical metric. However, the paper only evaluates performance on a binary classification task (normal vs. abnormal). It would strengthen the contribution if the model could also be evaluated on a regression task—i.e., directly predicting the LVEF score.\n3. The method for generating the heatmap shown in Figure 3 is not clearly described. More details are needed to understand how it was computed, including what features or gradients were used and how the visualization reflects model interpretability.", "was_revised": true, "final_justification": "Most of my concerns have been addressed. However, the contribution remains incremental in terms of novelty. Therefore, I keep my original score.", "rebuttal": "**Q1**. **Add Baseline for image self-distillation to ablation study.**\n\n**Answer.**  Thank you for your suggestion. To rigorously evaluate each component, we have added the baseline using only masked image  self-distillation to the loss ablation table (Table 4a). Indeed, we find that the image and video self distillation perform suboptimally in isolation, as shown in **Table 1**, confirming that spatial or temporal cues alone are insufficient for strong representation learning. In contrast, combining both with the SCD loss, which explicitly distills fine-grained semantic structure from the image branch into the video backbone, achieves the best results. This supports our intuition that SCD is crucial for aligning spatial semantics with temporal dynamics, enabling more robust and clinically meaningful video representations. We will add this to our ablation tables in the camera-ready version.\n\n**Table 1. Effect of the different loss terms on classification performance (Balanced Accuracy, Precision, and F1).**\n\n\n| $\\mathcal{L}^{vid}_{\\text{ssl}}$ | $\\mathcal{L}^{img}_{\\text{ssl}}$ | $\\mathcal{L}_{\\text{SCD}}$ | Bal. Acc. | Precision | F1 |\n|:--------------------------------:|:--------------------------------:|:-------------------------:|:---------:|:---------:|:--:|\n| **✓** | **✗** | **✗** | 52.27 | 53.16 | 48.23 |\n| **✗** | **✓** | **✗** | 53.66 | 55.22 | 49.43 |\n| **✓** | **✓** | **✓** | **63.20** | **65.35** | **61.45** |\n\n**Q2.** **Include Ejection Fraction downstream task**\n\n**Answer.** We thank the reviewer for the valuable suggestion to evaluate LVEF regression. We performed this analysis, and the results are presented below and in **Table 2**.\n\nFirst, to establish a direct comparison with other SSL baselines, we adopted a standard linear probing setup, training only a linear layer on top of frozen self-supervised features. As shown in Table 2, our DISCOVR model achieves a Mean Absolute Error (MAE) of 7.79, demonstrating superior performance over the baselines.\n\nTo further evaluate the strength of DISCOVR, we then finetuned only the last three blocks of our encoder. This significantly boosts performance, reducing the MAE to 6.32. This result is particularly compelling when compared to the state-of-the-art fully supervised model, EchoNet-Dynamic [1]. The multi-modal EchoNet-Dynamic model achieves an MAE of 4.05 by leveraging a large, multi-task architecture (ejection fraction regression + segmentation) of 71.1 million parameters that is co-trained with an extensive set of 20,060 manually segmented labels.\n\nFor a more direct and fair comparison to our single-task setup (LVEF prediction), we benchmarked our model against the EchoNet-Dynamic base model and other supervised baselines from [1] which are trained only on EF data. In this comparison, our self-supervised approach demonstrates a clear advantage. Our partially fine-tuned DISCOVR model (MAE 6.32) outperforms these fully-supervised counterparts, including MC3 (MAE 6.59), the base EchoNet-Dynamic model (MAE 7.35), and R3D (MAE 7.63). This demonstrates DISCOVR's learned representations effectively generalize to clinically relevant regression tasks such as ejection fraction (EF) prediction.\n\n**Table 2. LVEF prediction results on the EchoNet‑Dynamic dataset, detailing supervision type. Our self‑supervised method is compared against other SSL methods and fully‑supervised baselines from [1].**\n\n| Model | MAE&nbsp;↓ | RMSE&nbsp;↓ | EF Labels | Seg. Labels |\n|-------|:---------:|:-----------:|:---------:|:-----------:|\n| **Self‑Supervised (Linear Probing)** ||||| \n| VideoMAE | 8.02 | 11.16 | ✓ |   |\n| MGMAE | 8.88 | 12.47 | ✓ |   |\n| DISCOVR (Ours) | **7.79** | **10.89** | ✓ |   |\n| **Fully‑Supervised Baselines [1] (trained only with EF data)** ||||| \n| MC3 (All frames) | 6.59 | 9.39 | ✓ |   |\n| EchoNet‑Dynamic (All frames) | 7.35 | 9.53 | ✓ |   |\n| R3D (All frames) | 7.63 | 9.75 | ✓ |   |\n| DISCOVR (finetune last 3 blocks, 64 frames) | **6.32** | **8.62** | ✓ |   |\n| **Supervised with Segmentation & EF labels** ||||| \n| EchoNet‑Dynamic (Full model) | **4.05** | **5.30** | ✓ | ✓ |\n\n\n**Q3.** **How were visualisations in Figure 3 generated?**\n\n**Answer**. For Fig. 3, we use a self-attention map visualization method, similar to that of  DINO [2], to evaluate what each model attends to within the input video.  This technique extracts self-attention scores from the final layer of our video-encoder and projects them onto the video frames, providing a direct illustration of “what the model is looking at” as it forms its internal representation. The approach relies solely on the model’s native attention weights, without the use of gradients or class-specific features. This visualization highlights the spatial and temporal regions that are most influential for the model’s reasoning. In the case of our model, these regions correspond to clinically relevant cardiac structures and their motion throughout the heart cycle, supporting the interpretability and clinical utility of the learned representations. We will add the explanation to the figure's caption in the final version.\n\n**Q4.** **Discussion regarding societal impact and limitations.**\n\n**Answer.**\nWe appreciate this important feedback. In the broader impact section present in the appendix, we discuss that our method has the potential to assist sonographers in screening by flagging subtle abnormalities in echocardiography scans. However, as also noted, DISCOVR requires rigorous clinical validation before it can be used in clinical practice. This validation is essential for addressing the significant risks of inherent biases that could otherwise lead to unreliable or inequitable outcomes. For example, models must be assessed for population and demographic bias, where performance can differ across patient demographics [3,4], as well as machine and algorithmic bias, where a model may learn from site-specific imaging artifacts rather than true pathology [5,6]. Furthermore, AI can get biased towards the subjective and sometimes flawed cognitive biases of human experts, a form of operator and annotation bias that gets embedded in the training data [7]. Finally, issues like prevalence bias, where a model's predictive accuracy is skewed by the frequency of a disease in its training data, must also be carefully evaluated.\n\nTo this end, we are currently conducting prospective evaluations across five clinical sites in diverse geographical regions, with a particular focus on systematically identifying and analyzing model failure cases, evaluating performance across patient subgroups and specific congenital heart disease (CHD) conditions, and defining the boundaries of generalization. The outcome of this ongoing work, including limitations and potential risks relevant to clinical deployment, will be carefully documented and published. We will add a detailed description regarding the same in the camera-ready version.\n\n---\n\nWe thank the reviewer for their thoughtful comments and constructive suggestions. Your requests regarding the image self-distillation ablation, the LVEF regression task, and detailed evaluation metrics have led us to  expand our experiments and clarify both our implementation and broader implications. If any questions remain unresolved, we are happy to provide further clarification or engage in additional discussion.\n\n---\n## References \n\n[1] Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C.P., Heidenreich, P.A., Harrington, R.A., Liang, D.H., Ashley, E.A. and Zou, J.Y., 2020. **Video-based AI for beat-to-beat assessment of cardiac function.** *Nature, 580(7802), pp.252-256.*\n\n[2] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P. and Joulin, A., 2021. **Emerging properties in self-supervised vision transformers.** *In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9650-9660).*\n\n[3] Liu, Y., Jain, A., Eng, C., Way, D.H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S. and Gupta, V., 2020. **A deep learning system for differential diagnosis of skin diseases**. *Nature medicine, 26(6), pp.900-908.*\n\n[4] Gichoya, J.W., Banerjee, I., Bhimireddy, A.R., Burns, J.L., Celi, L.A., Chen, L.C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.C. and Kuo, P.C., 2022. **AI recognition of patient race in medical imaging: a modelling study.** *The Lancet Digital Health, 4(6), pp.e406-e414.*\n\n[5] Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J. and Oermann, E.K., 2018. **Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study.** *PLoS medicine, 15(11), p.e1002683.*\n\n[6] Howard, F.M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O.I., Kather, J.N. and Cipriani, N., 2021. **The impact of site-specific digital histology signatures on deep learning model accuracy and bias.** *Nature communications, 12(1), p.4423.*\n\n[7] Vrudhula, A., Kwan, A.C., Ouyang, D. and Cheng, S., 2024. **Machine learning and bias in medical imaging: opportunities and challenges.** *Circulation: Cardiovascular Imaging, 17(2), p.e015495.*"}, {"review_id": "m8SK1hDc1y", "reviewer": "Reviewer_Jw9e", "summary": "This paper introduces DISCOVR, a novel self-supervised learning (SSL) framework for echocardiography (cardiac ultrasound) videos. Echocardiography poses unique challenges due to subtle anatomical structures, high frame similarity, and low signal-to-noise ratios. DISCOVR addresses these by combining: Temporal modeling, Spatial semantics, and Cross-modal distillation. The extensive experiments on six datasets show its effectiveness compared to the SOTA SSL and anomaly detection methods.", "questions": "How effective of the proposed method in generalizing to other medical imaging modalities except for the echocardiography video?", "limitations": "The code is not ready for release yet during the submission and review period. And the reproduction instruction is not provided.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The research topic on the representation learning of echocardiographic video is meaningful and helpful for real-world applications.\n- The paper is well-structured and easy to read with clear formulations.\n- The proposed method, combining three loss components, is proven to be effective in the evaluation.\n- The experiments are comprehensive, covering diverse datasets and baselines.\n\nWeaknesses:\n\n- Lack of in-depth analysis on the contribution of each component to the final performance. For example, how do the encoded images help the video modality training? Some visualizations or case studies, e.g., the separation of hidden state features before and after adding the image modality, may help.\n- The ablation studies on the effect of components (i.e., Table 4) are insufficient to fully prove the effectiveness of each component of the proposed method. For example, the image SSL loss is ignored.\n- The direct addition of two modalities in Equation (9) lacks a convincing explanation, since normally, there exists a balance coefficient to test the best performance.", "was_revised": true, "final_justification": "My final recommendation and evaluation of this paper is 4: Borderline accept.", "rebuttal": "**Q1.** **Include image-SSL baseline and visualizations to understand the effect of image-guidance**\n\n**Answer.** To address the reviewer’s concern regarding the contribution of each modality in our framework, we have included explicit baselines in **Table 1** that isolate the performance of models using only image or only video self-distillation. Our results show that neither video-only nor image-only models achieve F1 scores above 50\\%, underscoring that neither modality alone is sufficient for strong video representation learning. In contrast, combining both modalities with our cross-modal SCD loss yields a significant improvement, resulting in 61.45\\% F1 and 63.20\\% balanced accuracy. This demonstrates that the integration of spatial and temporal encoding is essential and that our SCD mechanism is crucial for unifying these features to produce robust and clinically meaningful representations. Additionally, we have generated self-attention heatmaps (in the style of Figure 3) that qualitatively compare models with and without the image modality. These visualizations show that the inclusion of the image encoder helps the model focus more effectively on fine-grained, clinically relevant structures. Due to NeurIPS policy, we cannot include these figures at this stage, but we will make them available in the camera-ready version to provide further qualitative support for our method.\n\n**Table 1. Effect of the different loss terms on classification performance (Balanced Accuracy, Precision, and F1).**\n\n| $\\mathcal{L}^{vid}_{\\text{ssl}}$ | $\\mathcal{L}^{img}_{\\text{ssl}}$ | $\\mathcal{L}_{\\text{SCD}}$ | Bal. Acc. | Precision | F1 |\n|:--------------------------------:|:--------------------------------:|:-------------------------:|:---------:|:---------:|:--:|\n| **✓** | **✗** | **✗** | 52.27 | 53.16 | 48.23 |\n| **✗** | **✓** | **✗** | 53.66 | 55.22 | 49.43 |\n| **✓** | **✓** | **✓** | **63.20** | **65.35** | **61.45** |\n\n**Q2. Ablation of Loss Weight Terms** \n\n**Answer.** We agree with the reviewer about the potential importance of balancing the loss terms. In our experiments, simply combining the loss terms with equal weighting already yielded strong, state-of-the-art (SOTA) results. To further assess the effect of different weightings, we trained models for 200 epochs (as training a full model takes a long time and is computationally expensive) using several alternative loss weight combinations as shown in **Table 2**. While the differences in performance across these settings were not substantial, the configuration $\\lambda_{\\text{img}} = 0.8$ and $\\lambda_{\\text{vid}} = 0.2$ gave the best overall results, achieving 56.28\\% Accuracy, 56.27\\% Balanced Accuracy, and an F1 score of 56.14\\%. We thank the reviewer for the suggestion that led to increase in performance. We will include the complete analysis by training for the full 400 epochs with more combinations, as well as additional details, in the camera-ready version.\n\n**Table 2. Effect of loss weights ($\\lambda_{\\text{img}}$ for the image branch and $\\lambda_{\\text{vid}}$ for the video branch) on linear‑probing classification performance on the EchoNet‑Dynamic dataset.**\n\n| $\\lambda_{\\text{img}}$ | $\\lambda_{\\text{vid}}$ | Accuracy | Balanced Accuracy | F1 Score |\n|:----------------------:|:----------------------:|:--------:|:-----------------:|:--------:|\n| 1.0 | 1.0 | 55.52 | 55.51 | 55.47 |\n| 0.2 | 0.8 | 55.04 | 55.05 | 54.84 |\n| 0.2 | 0.5 | 55.80 | 55.76 | 54.64 |\n| 0.8 | 0.2 | **56.28** | **56.27** | **56.14** |\n| 0.5 | 0.2 | 55.60 | 55.57 | 55.02 |\n\n**Q3. Generalization to Other Modalities.**\n\n**Answer.**   Most medical imaging modalities, aside from echocardiography, consist primarily of static images or 3D volumes, which results in very limited availability of medical video datasets for research. For example, Cholec80 includes 80 laparoscopic surgery videos, HeiCo has 30 colorectal surgery videos, and EndoVis challenge datasets typically contain fewer than 50 videos per task. Hence, large-scale self-supervised pretraining and direct evaluation of DISCOVR on other medical video datasets are not feasible.\n\nTo test whether DISCOVR generalizes beyond echocardiography, we evaluated its transfer performance on two distinct medical image benchmarks: the Breast Ultrasound Images dataset [1] (cancer detection across 600 patients) and DermMNIST [2] (skin lesion classification). Both breast ultrasound and echocardiography require the detection of small, irregular regions of altered tissue, such as hypoechoic tumors in the breast or localized wall motion abnormalities in the heart, making the ability to identify subtle structural changes in one domain directly applicable to the other. Similarly, DermMNIST demands fine-grained visual discrimination between morphologically similar skin lesions. For both benchmarks, we froze the DISCOVR video encoder and trained a linear classifier, comparing performance directly across methods.\n\nAs shown in **Table 3**, our method demonstrates strong generalization across both tasks. On the Breast Ultrasound dataset, DISCOVR improves balanced accuracy by 2.01\\% over VideoMAE, 19.83\\% over SIGMA, and 12.01\\% over MGMAE. For DermMNIST, DISCOVR achieves an accuracy of 71.68\\%, outperforming VideoMAE by 2.85\\%, SIGMA by 3.00\\%, and MGMAE by 3.85\\%. These results demonstrate strong generalization to multiple medical image analysis tasks beyond echocardiography.\n\nFurther, to assess generalization to natural video data, we pretrained on 16 frame clips (default for the benchmark) and evaluated all models on the Kinetics 400 action recognition benchmark, using a zero-shot protocol where KNN classification with $K=20$ was applied to features using 16 frames  from the frozen video backbone. As shown in **Table 4**, DISCOVR achieves the highest Top-1 accuracy at 22.3\\%, outperforming MVD by 3.6\\%, MME by 3.2\\%, and VideoMAE by 1.6\\%, while also requiring the fewest pretraining epochs. These results highlight that DISCOVR not only excels at medical video tasks but also learns generalizable representations efficiently for large-scale natural video datasets.\n\n**Table 3. Linear‑probing results on the Breast Ultrasound dataset and DermMNIST.  \nFor Breast Ultrasound, we report Balanced Accuracy (Bal. Acc.) and F1; for DermMNIST, overall Accuracy (Acc.).**\n\n| Method | Breast US (Bal. Acc.) | Breast US (F1) | **┃** | DermMNIST (Acc.) |\n|--------|:--------------------:|:--------------:|:----:|:---------------:|\n| VideoMAE [3]        | 61.75 | 64.45 | **┃** | 68.83 |\n| SIGMA [4]              | 43.93 | 42.21 | **┃** | 68.68 |\n| MGMAE [5]           | 51.75 | 52.34 | **┃** | 67.83 |\n| **DISCOVR (Ours)**  | **63.76** | **65.44** | **┃** | **71.68** |\n\n**Table 4. Zero‑shot KNN classification performance on Kinetics‑400.**\n\n| Model | Epochs | Top‑1 Accuracy (%) |\n|-------|:------:|:------------------:|\n| MVD [6]            | 1600 | 18.7 |\n| MME [7]            |  800 | 19.1 |\n| VideoMAE [3]       |  800 | 20.7 |\n| **DISCOVR (Ours)** | **400** | **22.30** |\n\n\n**Q4. Code Release and Reproducibility**\n\n**Answer.** We appreciate the reviewer’s concern regarding reproducibility. We have already included our code in the supplementary materials for inspection. Additionally, we have released code and all necessary instructions for reproduction on anonymous GitHub repository but we are unable to share the repository link at this stage due to NeurIPS guidelines. Full access to the repository and detailed instructions will be provided in the camera-ready version to ensure easy reproducibility for the community.\n\n---\n\nWe thank the reviewer for their constructive feedback and specific suggestions regarding the image-SSL baseline, loss weighting, generalization to other modalities, and reproducibility. Your input prompted us to add explicit ablation baselines, experiment with alternative loss weightings, and expand our evaluation to external medical datasets and natural video benchmarks. We have also clarified our code release plans. If any questions remain unresolved, we are happy to provide further clarification or engage in additional discussion.\n\n---\n\n## References \n[1] Al-Dhabyani, W., Gomaa, M., Khaled, H. and Fahmy, A., 2020. **Dataset of breast ultrasound images.** *Data in brief, 28, p.104863.*\n\n[2] Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H. and Ni, B., 2023. **Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.** *Scientific Data, 10(1), p.41.*\n\n[3] Tong, Z., Song, Y., Wang, J. and Wang, L., 2022. Videomae: **Masked autoencoders are data-efficient learners for self-supervised video pre-training.** *Advances in neural information processing systems, 35, pp.10078-10093.*\n\n[4]  Salehi, M., Dorkenwald, M., Thoker, F.M., Gavves, E., Snoek, C.G. and Asano, Y.M., 2024, September. **Sigma: Sinkhorn-guided masked video modeling.** *In European Conference on Computer Vision (pp. 293-312). Cham: Springer Nature Switzerland.*\n\n[5] Huang, B., Zhao, Z., Zhang, G., Qiao, Y. and Wang, L., 2023. **Mgmae: Motion guided masking for video masked autoencoding.** *In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 13493-13504).*\n\n[6] Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Yuan, L. and Jiang, Y.G., 2023. **Masked video distillation: Rethinking masked feature modeling for self-supervised video representation learning.** *In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6312-6322).*\n\n[7] Sun, X., Chen, P., Chen, L., Li, C., Li, T.H., Tan, M. and Gan, C., 2023. **Masked motion encoding for self-supervised video representation learning.** *In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2235-2245).*"}, {"review_id": "1SfMUUcvRK", "reviewer": "Reviewer_fPeB", "summary": "This paper introduces DISCOVR, a self-supervised framework for echocardiography video representation learning. The method combines \nVideo self-distillation, Masked image self-distillation and Semantic Cluster Distillation (SCD) modules. DISCOVR is trained only on normal echocardiograms and evaluated on six datasets. The method consistently outperforms state-of-the-art video-SSL and anomaly-detection baselines.", "questions": "How is the computational cost and scalability compared to the competitive baselines?\n\nHow is the proposed method compared to a supervised model pretrained on labeled video datasets.\n\nIs there evidence showing that the method can be applied to other modalities?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n\nThe proposed method addresses the key weakness of prior SSL methods that ignore fine spatial detail or temporal coherence.\n\nExperiments cover diverse datasets and multiple downstream tasks, demonstrating robust generalization across populations and clinical settings.\n\nThe method shows consistent improvements over competitive baselines.\n\nWeakness:\n\nI have concerns about the novelty of the method. It looks like an incremental engineering, i.e., a well-executed application of existing techniques, rather than the proposal of a new learning framework. The idea of self-distillation method is proposed in DINO. The masked image self-distillation is closely related to VideoMAE. The Semantic Cluster Distillation is similar to the “Online cluster” methods such as SwAV and DINO’s Prototype Extension. Beyond combining three established SSL components, the paper offers limited insights that would advance our understanding of representation learning.\n\nThe paper does not discuss computational cost and scalability.\n\nThe method is not compared to a supervised model pretrained on large labeled video datasets.\n\nThe paper does not explore other ultrasound modalities beyond echocardiography.", "was_revised": true, "final_justification": "The authors have addressed my concerns. I did not identify other major issues in this paper.", "rebuttal": "**Q1. Novelty Clarification and Method Significance**\n\n**Answer.** While we appreciate the reviewer’s thoughtful analysis, we respectfully disagree with the assessment that DISCOVR is simply an assembly of existing techniques without novel contributions.\n\nIn DISCOVR, we explore the fundamental challenge of integrating detailed *spatial* information within frames and evolving *temporal dynamics* across sequences, which is essential for fine-grained video domains such as echocardiography. Previous self-supervised methods tend to address these aspects separately: image-based approaches like DINO and SwAV focus on static visual features, while temporal models like VideoMAE do not capture the complementary context provided by spatial representations.\n\nTo address this gap, DISCOVR introduces an online cross-modal (image-video) Semantic Cluster Distillation (SCD) loss that dynamically aligns spatial and temporal features using shared, evolving prototypes. This approach enables a continuous exchange of information between image and video representations and establishes a direct connection between modalities, which is not present in previous work.\n\nEmpirical results substantiate the impact of our design. DISCOVR outperforms both image-only and video-only self-distillation variants by more than 12 \\% F1, as shown in **Table 1** (Ablation Table 4a in main paper) . Our method is also systematically evaluated on six echocardiography datasets covering fetal, pediatric, and adult patient populations, and across a wide range of clinically meaningful tasks including anomaly detection, classification, segmentation, and ejection fraction estimation. Beyond echocardiography, DISCOVR demonstrates robust generalization by outperforming baselines on external breast ultrasound and skin cancer datasets, which involve distinct anatomical structures, acquisition protocols, and diagnostic objectives. We will make sure to explicitly state this in the introduction and method section of the camera-ready version.\n\n**Table 1. Effect of the different loss terms on classification performance (Balanced Accuracy, Precision, and F1).**\n\n| $\\mathcal{L}^{vid}_{\\text{ssl}}$ | $\\mathcal{L}^{img}_{\\text{ssl}}$ | $\\mathcal{L}_{\\text{SCD}}$ | Bal. Acc. | Precision | F1 |\n|:--:|:--------------------------------:|:-:|:-:|:-:|:--:|\n| **✓** | **✗** | **✗** | 52.27 | 53.16 | 48.23 |\n| **✗** | **✓** | **✗** | 53.66 | 55.22 | 49.43 |\n| **✓** | **✓** | **✓** | **63.20** | **65.35** | **61.45** |\n\n**Q2. Computational Cost and Scalability**\n\n**Answer.** We report the computational cost, as requested, in **Table 2**. The table shows both training and inference statistics, showing GPU memory usage and F1-score for each method on EchoNet-Dynamic, for a batch size of 1, 16 frames, and a spatial size of $112 \\times 112$. During training, DISCOVR uses slightly more GPU memory (10.5GB) compared to prior methods (between 9.0 and 9.5GB) but achieves a notable +6.38\\% improvement in F1-score over the closest competitor. At inference, all methods, including DISCOVR, use identical ViViT-like encoders, resulting in nearly the same GPU memory footprint and FLOPS. This demonstrates that our method’s performance improvements come with minimal extra training cost and no penalty for inference efficiency.\n\n**Table 2. Training and inference GPU memory, FLOPS, and F1‑score on EchoNet‑Dynamic (batch size = 1, 16 frames, 112 × 112 resolution).**\n\n| Model              | Train Mem (GB) | F1‑score | Infer Mem (GB) | Infer FLOPS |\n|-|:-:|:-:|:-:|:-:|\n| MGMAE              | 9.0  | 46.13 | 1.153 | 101.85 |\n| VideoMAE           | 9.0  | 55.07 | 1.153 | 101.85 |\n| SIGMA              | 9.2  | 49.04 | 1.153 | 101.85 |\n| Video‑distillation | 9.5  | 48.23 | 1.153 | 101.85 |\n| **DISCOVR (Ours)** | 10.5 | **61.45** | 1.153 | 101.85 |\n\n\n**Q3. How is the proposed method compared to a supervised model pretrained on labeled video datasets.**\n\n**Answer.**  As requested, we evaluated our approach against ViViT  and TimeSformer   models pretrained in a supervised manner on the Kinetics dataset, using publicly available checkpoints from HuggingFace and performing linear probing on our EchoDynamic labeled set. As shown in **Table 3**, DISCOVR outperforms ViViT and TimeSformer by nearly 8\\% and 4\\%, respectively, across balanced accuracy, F1-score, and accuracy. \n\nTo further demonstrate the effect of label scarcity in the medical domain, we also trained a ViViT model from scratch using only the available labeled data. As shown in **Table 4**, DISCOVR (with linear probing) improves over the supervised-from-scratch ViViT by over 24\\% in balanced accuracy (77.68\\% vs. 53.45\\%), 18\\% in precision (77.61\\% vs. 59.50\\%), and 34\\% in F1-score (77.63\\% vs. 43.96\\%). These results highlight both the challenge of transferring from natural to medical video domains and the significant advantage of DISCOVR’s self-supervised representations, especially under limited annotation.\n\n**Table 3. Linear Probing result comparison of models trained on Kinetics-400 large‑scale datasets vs DISCOVR on the EchoDynamic dataset.**\n\n| Model            | Accuracy | Balanced Accuracy | F1 Score |\n|-|:-:|:-:|:-:|\n| ViViT           | 69.76    | 69.75            | 69.74    |\n| TimeSformer      | 73.68    | 73.47            | 73.47    |\n| **DISCOVR (Ours)** | **77.68** | **77.61**        | **77.63** |\n\n---\n\n**Table 4. Comparison of fully supervised ViViT (trained from scratch) versus self‑supervised DISCOVR (linear probe on the same labeled data).**\n\n| Model                               | Balanced Acc. | Precision | F1‑Score |\n|-|:-:|:-:|:-:|\n| ViViT‑supervised (from scratch)     | 53.45         | 59.50     | 43.96    |\n| DISCOVR (linear probe)         | **77.68**     | **77.61** | **77.63** |\n\n**Q4. Evidence of Generalization to other Modality**\n\n**Answer.**   Most medical imaging modalities, aside from echocardiography, consist primarily of static images or 3D volumes, which results in very limited availability of medical video datasets for research. For example, Cholec80 includes 80 laparoscopic surgery videos, HeiCo has 30 colorectal surgery videos, and EndoVis challenge datasets typically contain fewer than 50 videos per task. Hence, large-scale self-supervised pretraining and direct evaluation of DISCOVR on other medical video datasets are not feasible.\n\nTo test whether DISCOVR generalizes beyond echocardiography, we evaluated its transfer performance on two distinct medical image benchmarks: the Breast Ultrasound Images dataset [1] (cancer detection across 600 patients) and DermMNIST [2] (skin lesion classification). Both breast ultrasound and echocardiography require the detection of small, irregular regions of altered tissue, such as hypoechoic tumors in the breast or localized wall motion abnormalities in the heart, making the ability to identify subtle structural changes in one domain directly applicable to the other. Similarly, DermMNIST demands fine-grained visual discrimination between morphologically similar skin lesions. For both benchmarks, we froze the DISCOVR video encoder and trained a linear classifier, comparing performance directly across methods.\n\nAs shown in **Table 5**, our method demonstrates strong generalization across both tasks. On the Breast Ultrasound dataset, DISCOVR improves balanced accuracy by 2.01\\% over VideoMAE, 19.83\\% over SIGMA, and 12.01\\% over MGMAE. For DermMNIST, DISCOVR achieves an accuracy of 71.68\\%, outperforming VideoMAE by 2.85\\%, SIGMA by 3.00\\%, and MGMAE by 3.85\\%. These results demonstrate strong generalization to multiple medical image analysis tasks beyond echocardiography.\nFurther, to assess generalization to natural video data, we pretrained and evaluated all models on the Kinetics 400 action recognition benchmark, using a zero-shot protocol where KNN classification with $K=20$ was applied to features using 64 frames from the frozen video backbone. As shown in **Table 6**, DISCOVR achieves the highest Top-1 accuracy at 22.3\\%, outperforming MVD by 3.6\\%, MME by 3.2\\%, and VideoMAE by 1.6\\%, while also requiring the fewest pretraining epochs. These results highlight that DISCOVR not only excels at medical video tasks but also learns generalizable representations efficiently for large-scale natural video datasets.\n\n**Table 5. Linear‑probing results on the Breast Ultrasound dataset and DermMNIST.  \nFor Breast Ultrasound, we report Balanced Accuracy (Bal. Acc.) and F1; for DermMNIST, overall Accuracy (Acc.).**\n\n| Method | Breast US (Bal. Acc.) | Breast US (F1) | **┃** | DermMNIST (Acc.) |\n|-|:-:|:-:|:-:|:-:|\n| VideoMAE         | 61.75 | 64.45 | **┃** | 68.83 |\n| SIGMA               | 43.93 | 42.21 | **┃** | 68.68 |\n| MGMAE            | 51.75 | 52.34 | **┃** | 67.83 |\n| **DISCOVR (Ours)**  | **63.76** | **65.44** | **┃** | **71.68** |\n\n**Table 6. Zero‑shot KNN classification performance on Kinetics‑400.**\n\n| Model | Epochs | Top‑1 Accuracy (%) |\n|-|:------:|:-:|\n| MVD             | 1600 | 18.7 |\n| MME             |  800 | 19.1 |\n| VideoMAE        |  800 | 20.7 |\n| **DISCOVR (Ours)** | **400** | **22.30** |\n\n---\n\nWe thank the reviewer for their valuable feedback and  insightful questions regarding novelty, scalability, and generalization. We have clarified the distinct contributions of our method and provided new results on computational efficiency and transfer. If any questions remain unresolved, we are happy to provide further clarification or engage in additional discussion.\n\n---\n\n## References \n\n[1] Al-Dhabyani, W., Gomaa, M., Khaled, H. and Fahmy, A., 2020. **Dataset of breast ultrasound images.** *Data in brief, 28, p.104863.*\n\n[2] Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H. and Ni, B., 2023. **Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.** *Scientific Data, 10(1), p.41.*"}, {"review_id": "Qi2Cs0hspc", "reviewer": "Reviewer_jDmx", "summary": "This paper introduces a self-supervised learning (SSL) framework designed for the analysis of echocardiography videos. The core of the proposed method is a novel 3D masked autoencoder that learns representations by reconstructing missing spatiotemporal tokens from cardiac cycles.", "questions": "1. The process of 3D tokenization requires further clarification. The paper states that raw patches are extracted, spatially downsampled to 112x112, and temporally processed with a stride of 3. However, this tokenization scheme may lead to a loss of critical information, especially when compared to more sophisticated methods like VAE-based tokenization. Given that cardiac motion is a key determinant in echocardiography analysis, could the authors provide a more detailed justification for this choice and comment on the potential impact of temporal information loss?\n\n2. The comparison to baselines needs to be clarified. The proposed approach is specifically trained on ultrasound images, whereas the other methods are general-purpose natural video SSL models. It is likely that these models have not been exposed to the unique data distribution of ultrasound videos. Were the baseline models pre-trained on a similar dataset of unlabeled echocardiography videos? If not, the observed performance gains may be attributable to the domain-specific pre-training rather than the novelty of the proposed architecture alone.\n\n3. The motivation (Fig. 1) for developing a new SSL framework for echocardiography is not entirely convincing. While it is true that natural video-pretrained models may not be optimal due to the significant distribution shift between natural images and ultrasound data, a more in-depth discussion on why existing SSL methods are insufficient would strengthen the paper's introduction.\n\n4. It is unclear how the proposed framework compares to more clinically-oriented, domain-specific approaches. For instance, how would the learned representations fare against methods that directly estimate clinical parameters like Left Ventricular Ejection Fraction (LVEF), as proposed by [1], or those that perform cardiac strain analysis by estimating motion in an unsupervised manner, as demonstrated by [2-3]? A comparison against these state-of-the-art, domain-specific methods would provide a more comprehensive evaluation of the proposed framework's effectiveness.\n\n[1] Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C. P., ... & Zou, J. Y. (2020). Video-based AI for beat-to-beat assessment of cardiac function. Nature, 580(7802), 252-256.\n\n[2] Zhi, D., Taha, A. A., & Ghesu, F. C. (2021, September). Unsupervised learning of myocardial strain from tagged mri. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 524-533). Springer, Cham.\n\n[3] Zhang, X., Stendahl, J. C., Staib, L. H., Sinusas, A. J., Wong, A., & Duncan, J. S. (2024, September). Adaptive Correspondence Scoring for Unsupervised Medical Image Registration. In European Conference on Computer Vision (pp. 76-92). Cham: Springer Nature Switzerland.\n\n5. To improve the clarity and reproducibility of the paper, it would be beneficial to define the input and output domains for each function in the mathematical formulation more explicitly. This would help readers to better understand the flow of data through the proposed model.", "limitations": "See weakness/questions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\nThe authors evaluated the proposed approach across two private datasets, along with several public datasets, and demonstrated consistent improvement.\n\nWeakness:\n1. It is unclear whether the baseline models, particularly those originally designed for natural videos, were trained using a comparable pipeline. For a fair comparison, it is crucial to understand if these baselines were also pre-trained on a large corpus of unlabeled echocardiography data, similar to the proposed framework.\n\n2. Several key design choices lack sufficient justification. A more detailed explanation of these decisions would improve the reproducibility and clarity of the work.", "was_revised": true, "final_justification": "The authors addressed most of my concerns and thus I raise my score to 4.", "rebuttal": "**Q1. Video Tokenization Clarification, explaining choice of spatial and temporal resolution**\n\n**Answer.** Thanks for bringing this into our attention. Our video tokenization begins by sampling 64 frames from each echocardiography video and grouping every two consecutive frames into tubelet tokens of size $2 \\times 16 \\times 16$, following the approach established in VideoMAE. For spatial resolution, we use $112 \\times 112$ pixels as it is the native resolution of our public datasets, including EchoNet-Dynamic, EchoNet-Pediatric, EchoNet-LVH, and CAMUS.  Also,our segmentation results, with Dice scores reaching up to 0.844, confirm that this setting is sufficient for capturing clinically meaningful cardiac structure.\n\nFor the temporal dimension, we sample frames with a stride of 3 to efficiently cover the high native frame rates typical of echocardiography, which range from 30 to 80 frames per second, and to represent the entire cardiac cycle without excessive redundancy. Unlike most computer vision self-supervised learning methods, such as VideoMAE, MGMAE, and SIGMA, that operate on clips of 16 frames, we use 64 frames as input. This enables our model to capture long-range temporal dependencies and evolving cardiac motion, which are crucial for detecting the subtle changes associated with clinical pathology. As shown in our ablation study in Table 4d, increasing the number of frames improves downstream performance, demonstrating that our temporal sampling strategy preserves the key information required for robust and clinically relevant video representation learning in echocardiography. We will include this information in the implementation details section of the paper to provide further clarity.\n\n**Q2. Data used for training baselines for fair-comparison**\n\n**Answer.**   All baseline models, except for RAD-DINO, were trained from scratch on the same echocardiography datasets as DISCOVR. RAD-DINO is a foundation model trained with proprietary data and therefore evaluated using its publicly released weights. For all other methods, we used the same video encoding, number of frames, stride, and other dataset-specific parameters. These details are described in the Experiment section. We will mention this protocol more explicitly in the camera-ready version of the paper.\n\n**Q3. Motivation (Fig. 1) for developing SSL framework is not clear.**\n\n**Answer.**  As shown in Figure 1 of the paper, echocardiography presents unique challenges for self-supervised learning. The left panel demonstrates that frame-to-frame similarity in ultrasound videos can be extremely high (mean cosine similarity up to 0.99 in this example), making it difficult for standard contrastive and augmentation-based SSL frameworks to construct meaningful positive and negative pairs. The right panel further emphasizes that clinically significant differences between normal and abnormal heart cycles are highly subtle, meaning that aggressive augmentations used in previous SSL methods can risk erasing these critical details entirely. Together, these observations highlight the need to not only capture fine-grained anatomical features but also robustly track how these structures evolve over time. Existing SSL methods, which frequently focus on either spatial information or temporal dynamics alone, may miss these crucial spatiotemporal clinical patterns.\n\nDISCOVR addresses these challenges by explicitly unifying spatial and temporal representation learning. Our semantic cluster distillation loss aligns fine-grained spatial features from the image encoder with temporal dynamics learned by the video encoder, enabling the model to capture both detailed structure and its evolution across the cardiac cycle. In accordance with your suggestion, we will revise both the introduction and Figure 1 caption to make these points more explicit.\n\n**Q4. Comparison with clinically-oriented, domain specific approaches for LVEF Estimation [1],  and Strain and Motion Analysis Methods ([2,3])**\n\n**Answer.**\n\n**LVEF Estimation ([1]):** As requested by the reviewer, we performed a comprehensive comparison of our approach against other self-supervised learning (SSL) methods and the fully-supervised method presented in [1] on the task of Left Ventricular Ejection Fraction (LVEF) prediction using the EchoNet-Dynamic ejection fraction dataset.\n\nFirst, to establish a direct comparison with other SSL baselines, we adopted a standard linear probing setup, training only a linear layer on top of frozen self-supervised features. As shown in **Table 1**, our DISCOVR model achieves a Mean Absolute Error (MAE) of 7.79, demonstrating superior performance over the baselines.\n\nTo further evaluate the strength of DISCOVR, we then finetuned only the last three blocks of our encoder. This significantly boosts performance, reducing the MAE to 6.32. This result is particularly compelling when compared to the state-of-the-art fully supervised model, EchoNet-Dynamic [1]. The multi-modal EchoNet-Dynamic model achieves an MAE of 4.05 by leveraging a large, multi-task architecture (ejection fraction regression + segmentation) of 71.1 million parameters that is co-trained with an extensive set of 20,060 manually segmented labels.\nFor a more direct and fair comparison to our single-task setup (LVEF prediction), we benchmarked our model against the EchoNet-Dynamic base model and other supervised baselines from [1] which are trained only on EF data. In this comparison, our self-supervised approach demonstrates a clear advantage. Our partially fine-tuned DISCOVR model (MAE 6.32) outperforms these fully-supervised counterparts, including MC3 (MAE 6.59), the base EchoNet-Dynamic model (MAE 7.35), and R3D (MAE 7.63). \n\nThis demonstrates DISCOVR's learned representations effectively generalize to clinically relevant regression tasks such as ejection fraction (EF) prediction.\n\n**Table 1. LVEF prediction results on the EchoNet‑Dynamic dataset, detailing supervision type. Our self‑supervised method is compared against other SSL methods and fully‑supervised baselines from [1].**\n\n| Model | MAE&nbsp;↓ | RMSE&nbsp;↓ | EF Labels | Seg. Labels |\n|-|:-:|:-:|:-:|:-:|\n| **Self‑Supervised (Linear Probing)** ||||| \n| VideoMAE | 8.02 | 11.16 | ✓ |   |\n| MGMAE | 8.88 | 12.47 | ✓ |   |\n| DISCOVR (Ours) | **7.79** | **10.89** | ✓ |   |\n| **Fully‑Supervised Baselines [1] (trained only with EF data)** ||||| \n| MC3 (All frames) | 6.59 | 9.39 | ✓ |   |\n| EchoNet‑Dynamic (EF, All frames) | 7.35 | 9.53 | ✓ |   |\n| R3D (All frames) | 7.63 | 9.75 | ✓ |   |\n| DISCOVR (finetune last 3 blocks, 64 frames) | **6.32** | **8.62** | ✓ |   |\n| **Supervised with Segmentation & EF labels** ||||| \n| EchoNet‑Dynamic (Full model) | **4.05** | **5.30** | ✓ | ✓ |\n\n**Strain and Motion Analysis Methods ([2,3]):**\nCitation [2] does not appear in the MICCAI 2021 proceedings or major academic databases, so a comparison was not possible. As for [3], it addresses general 3D medical image registration, but does not involve cardiac strain estimation, cardiac motion analysis, echocardiography, or self-supervised video representation learning. Therefore, it is not relevant for benchmarking our approach, which is specifically designed for clinically meaningful echocardiographic video tasks such as video anomaly detection, ejection fraction estimation, and cardiac strain analysis.\n  \n**Q5. Input and Output Domains for each function for better clarity**\n\n**Answer.** We have added **Table 2** that defines the input and output domains for each major function in our mathematical formulation, using the same symbolic notation as the main text for full clarity and reproducibility.\n\n**Table 2. DISCOVR training pipeline (symbolic overview).**\n\n| # | Block | Input $\\mathcal X$ | Output / mapping $\\mathcal Y$ |\n|:-:|-|-|-|\n| 1 | Video sampler | $\\text{idx}\\in\\{0,\\dots,\\|\\mathcal D\\|-1\\}$ | $x\\in\\mathbb R^{1\\times3\\times T_f\\times H\\times W}$ |\n| 2 | Multi‑local masker | $(\\mathcal B,\\mathcal N,\\mathcal V,\\rho_g,\\rho_\\ell)$ | $m_g\\in\\{0,1\\}^{\\mathcal B\\times\\mathcal N}$, $m_\\ell^{(v)}\\in\\{0,1\\}^{\\mathcal B\\times\\mathcal N}$ |\n| 3 | Student vid‑enc $E_s$ | $(x\\odot m,\\,m)$ | $z_s\\in\\mathbb R^{\\mathcal B\\times N_{\\text{vis}}\\times d_e}$, $N_{\\text{vis}}=(1-\\rho_g)\\mathcal N$ |\n| 4 | Teacher vid‑enc $E_t$ | $x$ (unmasked) | $z_t\\in\\mathbb R^{\\mathcal B\\times N_{\\text{vis}}\\times d_e}$ |\n| 5 | EMA update | $(\\theta_t,\\theta_s,\\lambda)$ | $\\theta_t\\leftarrow\\lambda\\theta_t + (1-\\lambda)\\theta_s$ |\n| 6 | Vid self‑distill | $(z_t,z_s)$ | $\\mathcal L_{\\text{vid}}=\\mathrm H\\big(\\operatorname{softmax}(z_t/\\tau_t),\\operatorname{softmax}(z_s/\\tau_s)\\big)$ |\n| 7 | Student img‑enc $I_s$ | $\\{f\\odot\\mu\\}$ | $h_s\\in\\mathbb R^{\\mathcal B\\times d_e}$ |\n| 8 | Teacher img‑enc $I_t$ | $f$ | $h_t\\in\\mathbb R^{\\mathcal B\\times d_e}$ |\n| 9 | Img self‑distill | $(h_t,h_s)$ | $\\mathcal L_{\\text{img}}=\\mathrm H\\big(\\operatorname{softmax}(h_t/\\tau_t),\\operatorname{softmax}(h_s/\\tau_s)\\big)$ |\n|10 | Token decoder $\\psi$ | $\\operatorname{proj}(z_s)$ | $\\hat y\\in\\mathbb R^{\\mathcal B\\times N_{\\text{mask}}\\times d_d}$, $N_{\\text{mask}}=\\rho_g\\mathcal N$ |\n|11 | Prototype proj. | $(\\hat y,h_t,P\\in\\mathbb R^{\\mathcal K\\times d_d})$ | $s_v=\\hat yP^\\top/\\tau,\\;s_i=h_tP^\\top/\\tau$ |\n|12 | Sinkhorn | $(s_v,s_i,\\varepsilon,T)$ | $q_v=\\operatorname{Sinkhorn}(s_v)$, $q_i=\\operatorname{Sinkhorn}(s_i)$ |\n|13 | SCD loss | $(s_v,q_i,s_i,q_v)$ | $\\mathcal L_{\\text{scd}}=\\mathrm{CE}(s_v,\\text{sg}(q_i))+\\mathrm{CE}(s_i,\\text{sg}(q_v))$ |\n|14 | Total loss | $(\\mathcal L_{\\text{vid}},\\mathcal L_{\\text{img}},\\mathcal L_{\\text{scd}})$ | $\\mathcal L_{\\text{tot}}=\\omega_{\\text{vid}}\\mathcal L_{\\text{vid}}+\\omega_{\\text{img}}\\mathcal L_{\\text{img}}+\\omega_{\\text{scd}}\\mathcal L_{\\text{scd}}$ |\n\nWe thank the reviewer for their detailed feedback. Your questions on tokenization, baselines, motivation, and clinical benchmarks prompted us to clarify our methods and expand our evaluation. We welcome any further questions or discussion."}]

Author rebuttal:

Meta-review: This paper received all positive reviews. After discussion, reviewers agreed to accept this paper.  The authors are encouraged to address the major concerns in the camera-ready version.
1. Add experimental evaluation on regression tasks.
2. Add ablation studies to evaluate each component in the model
3. Add results on computational cost

The authors’ rebuttal and messages were carefully ready, discussed, and considered.
Decision: Accept (poster)