Revisiting Data Augmentation for Ultrasound Images
Authors: Adam Tupper
Christian Gagné
Venue: Accepted by TMLR ()

Abstract: Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.

Published in Transactions on Machine Learning Research (07/2025)
Revisiting Data Augmentation for Ultrasound Images
Adam Tupper
odam. @eulaval.co
Institut Intelligence et Données (IID), Université Laval, Mila
Christian Gagné
christian.gugmed@gee..o
Institut Intelligence et Données (IID), Université Laval
Canada-CIFAR AI Chaär, Mila
Reviewed OpenReview:
https://openrevieu https: // openreview. net/ serforrm?? id= tGe
Abstract
Data augmentation is widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation tech- niques across different tasks and modalities. One modality where this especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques improving model performance across wide range of ultrasound image analysis tasks. To achieve this, we introduce new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images some cases. We also show that diverse augmentation using TrivialAugment, which widely used for natural images, is also effective for ultrasound images. Morcover, our proposed methodology repre- sents structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
1 Introduction
Data augmentation is an essential component of deep learning. It not only improves generalization, but it also core component of many self- and semi-supervised learning algorithms. However, while data augmentation is ubiquitous for training deep neural networks on natural images (i.e., images of human-scale scenes captured by ordinary digital cameras), when comes to training such models on medical images its proper usage is not common and clearly understood (Chlap et al.) 2021 Garcea et al. 2023 This is despite the difficulties we face collecting sulficient, data due to privacy protections and high acquisition and annotation costs.
The under-utilization of augmentation when working with medical images suggests a weaker understanding of the effectiveness of different operations and strategies. Often, we simply apply photometric and geometric transformations proposod from natural images as is, without rigorous testing. However, the low uptake indicates that. findings from natural images may not translate well to medical images. This not surprising given that the size of the objects of interest and the relevance of specific textures may differ significantly for doing detection, classification segmentation tasks from natural images compared to other modalities such as microscopy, X-ray, and ultrasound, to name few.
A lack of comparative studies featuring controlled experiments that evaluate various techniques over different tasks, datascts, and imaging modalitics has created gap in our understanding of data augmentation for medical images. While there are several excellent literature surveys on this topic (Chlap et al. 2021 Garcea et al. 2023), relying solely surveys leaves at risk of falling foul of publication bias (i.e., the dile-drawer

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effect). In addition, these surveys highlight the difficulty in drawing conclusions on which transforms are most effective, since there are many confounding variables. Ultimately, drawing conclusions from literature surveys alone is not enough. This problem needs to be addressed more rigorously and systematically through an experimental approach. In this work, we evaluate the effectiveness of data augmentation techniques for deep neural nelworks in ultrasound image analysis ! Our investigation reveals several key findings that provide practical guidance for implementing data augmentation in ultrasound image analysis:
Traditional domain-indepenn inneeeddend augmentations are effective, even more so in many cases than ultrasound-specific augmentations. They can be leveraged achieve quick performance improve- ments before investing time and resources in developing custom techniques.
2. The impact of individual augmentations varies substantially across both domains (cardiac vs. liver ultrasound) and tasks (classification vs. semantic segmentation), with notable differences even be- tween similar tasks on the same datasct.
3. While these variations might suggest the need for careful lask-specific luning of augmentation strate- gies, we find that applying a diverse set of augmentations using the simple Trivial Augment strategy [Müller & Hutter 2021) achieves substantial performance gains with limited tuning of the augmen- tation set.
The remainder of this paper is organized as follows. First, we discuss the prevalence of data augmentation for ultrasound image analysis using deep learning, ultrasound-speccffcc augmentations, and previous studies of data augmentations. Second, we present our benchmark that serves as the foundation for our analyses. Third, we provide in-depth description of the ultrasound-specffee augmentations included in our study. Fourth, present our analyses of individual augmentations and TrivialAugment. Finally, discuss the implications of our results.
2 Background
As previously mentioned, the use of data augmentation for ultrasound imaging is far less common than for natural image analysis. We start by presenting concrete analysis supporting this observation, then discuss proposals for ultrasound-specifie data augmentations, and finally examine prior studies comparing the efficacy of different data augmentations for medical image analysis.
2.1 Data Augmentation in Ultrasound Image Analysis
To understand data augmentation practices for ultrasound image analysis with deep learning, we analyzed
the use of data augmentation on 10 different publicly available ultrasound image datasets (Xu et al. 2023
Butterfly Network 2018 Leclerc et al.] 2019 Byra et al.] 2018 Basu et al. 2022 Zhao et al. 2023 Singla
jet al.
2023 Born et al. 2021 Chen et al. 2024 Stanford AIMI Center 2021 covering 11 regions of the
body. We describe these datasets comprehensively in the following section as they form the basis of our benchmark. For now, we focus on the snapshot they provide of the use of data augmentation in ultrasound imaging.
Of the 557 citations of the datasets catalogued by the Clarivate Web of Science platform as of August 2024, we identified 165 studies that used these datascts to train deep neural network models for classification and segmentation tasks. Among these studies, more than half (85 of 165) used no data augmentation al all when training their models. Out of those remaining, 48 used three or less augmentations and only only 13 used six or more. This pales in comparison to the large sets of 14 augmentations used in common data augmentation strategies such as AutoAugment (Cubuk el. al. 2019 RandAugment (Cubuk el al. 2020 and Augment (Müller & Hutter 2021
Our code, documentation and benchmark available http:////ttiiccccaaaaatppptttetp itgp:
vun xebofacience
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Figure 1: The popularity of different augmentation techniques among 165 studies from the ultrasound liter- ature, showing moderate adoption of common methods but limited adoption of aitrasound-speciff methods.
Upon examining the popularity of different augmentations, the list is dominated by natural image augmenta- tions commonly found in deep learning frameworks. As presented in Fig. I the most popular augmentations are classic geometric transforms that are known to perform well for natural image tasks, such as image flip- ping, rotation, zoom/scaling, random cropping, and translation. However, even among these most popular techniques, there is steep decline in their use. The first augmentations designed specifically for ultrasound images, that is Gaussian shadowing (Smistad et al.] 2018), haze artifact addition, depth atlenuation and speckle reduction (Ostvik et al., 2021), are used in only three or four articles. In fact, these are the only ultrasound-specffic augmentations used in multiple studies in our sample. We discuss these, along with other speeefic augmentations, in the following section.
Another interesting observation is the lack of adoption of "modern" data augmentation strategies (e.g.. RandAugment, Augmen,,,,me,,, etc.) among these studies, suggesting skepticism surrounding their efficacy from researchers working on medical image analysis using deep learning. Instead, a common pattern we found is that researchers tend to focus on simple, hand-crafted, fixed sequences of augmentations that reflect plausible differences that might arisc in real-world settings. This is despite the fact that these stronger, "unrealistic" straeege have proved effective other modalities. The strategies adopted in deep learning for medical image analysis are reminiscent of the strategies used a decade or more ago for general computer vision. This gap may reffect that ultrasound imaging simply lags behind natural image processing in adopting more recent techniques. Our work demonstrates the effectiveness of one such modern augmentation strategy for ultrasound image analysis, aiming to provide evidence that encourages wider adoption of these mot in the field.
2.2 Srrrasound-Speccife Data Augmentation
While domain-independden augmentations are the most frequently used, a variety of ultrasound-specifee techniques have also been proposed. These target unique charactcristics of ultrasound images to better simulate different machines and imaging conditions.
A common focus is noise manipulation. Techniques such as Multi-Level Speckle Noise MMonkam et al. 2023 Speckle Distortion (Ramakers et al.] 2024), and Speckle Noise (Wang ct al. 2022) all try to simulate realistic speckle noise, while Speckle Noise Suppression (Monkam et al. (023) and Speckle Reduction (Ostvik el al., 2021) aim to reduce it. Additional methods, including Haze Artifact (Stvii et al. 2021 and multiplicative noise simulate other realistic types of noise. Other methods simulate occlusions or variations in the field of view, similar to Cutout (DeVries & Taylor] 2017) and random cropping. Some darken regions to mimic
acoustic shadows (Smistad al. 2018 Singla et al. 2022 Ramakers et al. 2024). while Fan-Shape Preserv-
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ing Zoom (Singla et al. 2022). Field-of- View Masking (Pasdeloup et al. 2023 and Fan- Pan-Preserving Crop [Ramakers et al. 2024) all reduce the field of view.
To account for variability in probe orientation, Cone Position Adjustment and Perspective Position Ad- justment (Sfakianakis et al.) 2023 simulate changes in angle and rotation. Other methods adjust image intensity and contrast. These include Myocardium Intensity Adjustment (Sfakianakis et al. 2023), which
enhances specific cardiac regions, Tirindelli et al.Js (2021) signal-to-noise ratio augmentation, which modifies the relative intensity of spinal structures, and Non-linear colour mapping (Pasdcloup et al. 2023 Similarly,
Singla et al. 2022 transform images into "speckle parameter maps" to emphasize different tissue structures.
Some augmentations are variations of general image mixing methods, like mixup (Zhang et al. 2018) and CutMix (Yun et al. 2019). Prequency-Domain Mixing (Ding & Han| 2024) mixes images in the frequency do- mains, while Mixed- -xxample (Lee et al., 2021) mixes images according to specific patterns. Others replicate ultrasound-spocffic artifacts like depth attenuation (Ostvik et al. 2021 Singla et al.) (022), reverberation
[Ramakers et al., 2024 Tirindelli et al. )021), or deformation (Tirindelli et al.) 2021 Ramakers et al. 2024).
Despite their potential, many of these augmentations face barriers to adoption. Some are domain-specific or require precise segmentation maps, while others lack public implementations or have only been evaluated on narrow set of tasks. Although our focus is on data augmentation, it would be amiss to ignore the large body of research on synthetic data generation using generative models (Kebaili et al.) 2023 This offers a complementary approach, though it introduces its own challenges, such as generating accompanying segmentation masks or focusing on spurious correlations (e.g., correlation with medical devices) rather than relevant features. These issues aside, augmentation and synthetic data could significantly enhance model robustness and generalization when used together.
2.3 Previous Studies
Despite the proven benefits of data augmentation, there are few comparative studies evaluating which tech- niques and strategies are most elfective for medical images, despite the fragmented usage patterns and extensive literature on specialized methods. This has led to calls for more such studies (Garcea et al.) 2023). Our study extends these previous studies in several notable ways. First, only a single previous study in- cluded ultrasound images (Rainio & Klén 2024) and was limited to a single task. Furthermore, in this case and other studies on different imaging modalities (Bali & Mahara 2023 Castro et al., 2018 Hackal et al., 2021 Hussain et al.] 2018: [ama al.) 2019), the effectiveness of each augmentation was only tested when used offiin that is, used once before training to increase the size of the training set. In the case of [L et al. [2021), augmentation policies were learned via policy learning algorithm, but the effectiveness of individual augmentations within the defined search space was not assessed. The same goes for [Lu et al. h0233 who proposed an alternative augmentation stralegy to TrivialAugment. Finally, [attn-Rosen et al. 2018 compared the effectiveness their own sampling mixing augmentation against mixup (Zhang et al.
2018
which is frequently used in natural image settings.
3 UltraBench
One of the limitations of previous studies is a lack of comparisons across multiple domains. This challenge arises from the absence of ultrasound image analysis tasks in existing medical image analysis benchmarks, such as MedMNIST (Yang et al. 2023), MedSegBench (Kuş & Aydin 2024) and the Medical Segmentation Decathlon AAttonllli et al., 2022). To assess different augmentations for ultrasound image analysis, we created a benchmark of ultrasound image analysis tasks. The benchmark includes 14 tasks (7 classification, 7 segmentation) from 10 public datascts of 2D "fan-shape" ultrasound images captured with either convex and phased array ultrasound probes. These were used in the previous analysis of data augmentation usage and cover 11 regions of the body.
The following subsections outline the tasks defined within each datasct. Except for cases where data splits are predefined by the dataset's original authors, we split each dataset into training, validation, and test images using 7:1:2 split, using patient identifiers where applicable to ensure that there is patient overlap
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Figure 2: The class distributions for each image classification task included in UltraBench.
between the sets. Fig. shows the class distribution for each classification task, while Fig. 3 provides examples of the images and segmentation masks for each segmentation task.
We also created approximate scan segmentation masks for each dataset using morphological operations support the ditrasound-specffit augmentations described in the following scction. Examples of the masks generated for each dataset are provided in Appendix A
Annotated Ultrasound Liver The Annotated Ultrasound Liver (AUL) dataset (Xu ct al. 2023 consists of 735 images, including 435 with malignant masses, 200 with benign masses, and 100 with no masses. Each image is of a different patient, with a mean width of 945.33 px (a:: 142.46 px, min: 440 px, max: 1388 px) and height of 713.80 px (a: 94.81 px, min: 341 px, max: 910 px). With the exception one image that missing the outline of the liver, each image annotated with the outline of the liver and the outline the masses (if present). In addition, each image labeled malignant, benign, or normal according to the presence of malignant, benign, no masses in the image, respectively.
We define two segmentation tasks and classification task the AUL dataset: liver segmentation task using the 734 images with liver annotations, mass segmentation task on all 735 images, and mass classification task to classify the images according to the type of mass present in the images.
The Butterfly dataset (Butterlly Network 2018) was released for the 2018 MIT Grand Hack. It consists ultrasound images multiple body regions acquired using the Butterfly iQ point-of-care ultrasound device from 31 patients. The images are divided into nine groups according to the organ being imaged (morison's pouch, bladder, heart (PLAX view), heart (4-chamber view), heart (2-chamber view), IVC, carotid artery, lungs, and thyroid). We use these labels define ninc-class image classification task. In total, the dataset consists of 41,076 images, 34,325 of which are allocated to training and validation, while 6751 are reserved for testing. The images have an average width of 415.57 px (a: 31.16 px, min: 360 px, max: 462 px) and height of 500.80 px (a: 36.16 px, min: 384 px, max: 512px). We split the training and validation images using an 80:20 split into training and validation sets, ensuring that there is no patient overlap between the sets.
Butterfly
CAMUS The Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset
Teelerc al. 2019) consists of apical four-chamber and two-chamber view cardiac ultrasound sequences
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Stanford Thyrald
PSPH5
Figure 3: An example image and segmentation mask for each segmentation task included in UltraBench.
from 500 patients for total of 19,232 images. The metadata provided with each image includes segmenta- tion masks for the left. ventricle endocardium, myocardium, and the left. atrium. Each image also labeled according the quality of the scan (poor, medium, and good). The images have an average width of 597.58 px (o: 102.80 px, min: 323 px, max: 1181 px) and an average height of 491.58 px (a: 77.83 px, min: 292 px, max: px). For the CAMUS dataset, we include two tasks: image quality classification, as was explored by Nazar et al. (2024). and cardiac structure segmentation, the original segmentation task.
Fatty Liver The Dataset B-mode fatty liver ultrasound images Byra et al. 2018 referred to simply as the Fatty Liver dataset from here on, contains 550 liver ultrasound images from 55 patients, with 38 suffering from non-alcoholic fatty liver disease (NFLD; defined as >5 % of hepatocytes having fatty infiltration). The images all have resolution of 436 x 636 pixels. The associated binary classification task is classify the images into normal and NFLD.
The Gallbladder Cancer Ultrasound (GBCU) dataset (Basu et al. 2022 contains total of 1255 annotated abdominal ultrasound images (consisting of 432 normal, 558 benign, and 265 malignant images) collected from 218 patients (71 normal, 100 benign, and 47 malignant). The images have an average width of 1204.95 px (a:: 85.43 px, min: 854 px, max: 1156 px) and an average height of 854.64 px (a: 36.11 px, min: 688 px, max: 947 px). The dataset is already split into training and testing sets containing 1133 and 122 images, respectively. We further split the training set into training and validation sets using an 90:10 split. While there is no patient overlap between the training and test sets, cannot guarantee that there is patient overlap between the training and validation sets since all patient information removed before the dataset was published. The associated task is to classify the images according to the three classes.
GBCU
MMOTU The Multi-Modality Ovarian Tumor Ultrasound (MMOTU) dataset Zhao et al. 2023 is an ovarian cancer dataset consisting of 2D ultrasound and contrast-enhancee ultrasonography images. In this case, we are interested only in the ultrasound images. In total, there are 1469 2D ultrasound images, each accompanied by semantic segmentation masks that identify the tumor in the image. In addition, each image is labeled according the presence of each type of tumour (chocolate cyst, serous cystadenoma, teratoma, theca cell tumour, simple cyst, normal overy, mucinous cystadenoma, and high grade serous cystadenocarcénooma)) This allows us to define two tasks this dataset: binary tumour segmentation and multi-class tumour type classification. As is the of the GBCU dataset, this dataset is pre-split into training and testing sets, with 1000 examples collected from 171 patients in the training set and 469 examples collected from 76 patients in the Lest set. We further split the training set into training and validation sets using an 80:20 split. However, as all patient information has been removed from the dataset, we cannot guarantee the absence of patient overlap between the training and validation subsets. The images have an average width of 550.84 px (a: 55.38 px, min: 266 px, max: 794 and an average height of 762.04 px (a: 238.06 px, min: 302 px, max: 1135 px).

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Open Kidney The Open Kidney Ultrasound dataset (Singla et al. 2023) consists of 514 B-mode kidney ultrasound images, each from distinct patient. The images are annotated with kidney capsule pixel masks, which permits two separate semantic segmentation tasks: kidney capsule and a more fine-grain kidney regions segmentation. However, the limited amount of data relative to the complexity of the region segmentation lask means that training informative, effective models this setting not possible. The images have an average width of 1061.92 px (o: 200.44 px, min: 640px, max: 1920 px) and an average height of 773.71 px (o: 94.88 px, min: 480 px, max: 1080 px). To minimize distribution drift between the splits, we stratify by view (transverse, longitudinal, and other) when creating the training, validation, and test splits.
POCUS The Point-of-care Ultrasound (POCUS) dataset (Born et al. 2021 is collection of convex and linear probe lung ultrasound images and videos from different sources that was created for the diagnosis of COVID-19. We use the 142 convex probe videos and 29 convex probe images distributed by the authors and follow the procedure described in their original paper to process them, sampling the videos at a rate of 3 Hz, up to a maximum of 30 frames, and grouping the frames by video to prevent data leakage ween the train, validation, and test splits. In total, we extract 2726 examples. Each image is labeled by the pathology (healthy, pneumonia, covid) yielding three-class classification problem. The images have an average width of 499. 22 px (o: 205.39 px, min: 139 px, max: 1280 px) and an average height of 462.84 px (a: 167.07 px; min: 139 px, max: 1080 px).
The PSFHS dataset (Chen et al. 2024) is a dataset for fetal head and pubic symphysis segmen- tation, comprising 1358 images from 1124 patients. Each image is accompanicd by pixel-level segmentation masks for the fetal head and pubic symphysis, supporting a three-class image segment alinn ation task (luckground, pubic symphysis, and fetal head). The images all have resolution of 256 x 256 px.
PSFHS
Stanford Thyroid The Stanford Thyroid Ultrasound Cine-clip dataset (tanford AIMI Center 2021 referred to simply as the Stanford Thyroid dataset from here on, is a dataset of 192 thyroid nodule ultrasound cine-clips (vidcos) collected from 167 paticnts. The images in each sequence are associated with pixel- level nodule segmentation masks, patient demographics, lesion size and location, TI-RADS descriptors, and histopathological diagnoses. We use the nodule masks for a thyroid nodule segmentation task. In total, there are 17,412 images all with a resolution of 1054 802 px.
4 Ultrasound Image Augmentations
Of the ultrasiund-speecfic augmentations presented in Fig. D very few have been used beyond the original works. The exceptions are the depth attenuation, haze artifact, and speckle reduction augmentations pro- posed by Ostvik et al.] (2021), and the Gaussian shadow augmentation proposed by Smissad et al. (2018). However, their usage still pales in comparison to the most popular augmentations. These augmentations may see limited use not only due to their absence from standard libraries (e.g., Torchvision, MONAT, Al- bumentations) and lack of open-source implementations, but also because their effectiveness has not been widely demonstrraedd. To address these barriers, we provide implementations compatible with these libraries based on the original articles' descriptions, which are detailed below. The implementations are provided as a Python package alongside our source code.
4.1 Depth Attenuation
The depth attenuation augmentation proposed by Ostvik al. (2021) is designed to mimie the loss of energy of the ultrasound wave energy it moves through the body, which results in gradual drop in intensity with distance from the probe. In Ostvik et al. (2021), this is implemented as applying a "varying degree of intensity attemuation along the radial direction". Guided by the visualizations of the attemuation maps in their paper, and knowing that the intensity of the wave should decrease exponentially with distance, we implement the augmentation as follows.
Assuming the ultrasound fan is oriented such that the probe positioned at the middle-top of the image, we create an attenuation map that is used to scale the intensity of each pixel of the ultoooound scan mask
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Original Image
Attenuation Map
Augmented Image
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Figure 4: An example of the depth attemuation augmentation the AUL dataset with maximum attemu-
ation (A) of 0 and attenuation rate (μ) of 1.5.
Original Image
Haze Image
Augmented Image
Figure An example of the haze artifact augmentation on the AUL dataset with a radius r = 0.5 and 0.1.
S in the original image illustrated in Fig. The resulting image I' is given by I' y) A(x, v) @ S(z, v) @ 1(x
(1)
The attenuation map A is calculated as
A(z, v) = (1- exp(-ud) + +
(2)
where d = (a 0.5)² + 2². The maximum attemuation À and attenuation rate j are configurable parame- ters. By default, À is set to 0 and to generate variation sampled uniformly from the range (0,3).
4.2 Haze Artifact Addition
Acoustic haze is a semi-static noise band that is sometimes present in ultrasound images. To mimic this, Ostvik al.] (2021) proposed haze artifact augmentation that applics static with Gaussian profile at a fixed distance (radius) from the probe. Guided by their illustrations, we implement this augmentation by generating haze image H that is added to the pixels that lie within the ultrasound scan mask S in the original image I.
For a given haze radius r and standard deviation a that controls the spread of the noise, the haze image is calculated as
H(x, y) = -" exp( (d (d-r)² r)²
(3)
202
where d = V(x (x 0.5)² + y² and u ~ (0, 1). This results an image similar to that shown Fig. default, (0.05, 0.95) and (0000.1)..
By
4.3 Gaussian Shadow
To mimic acoustic shadows that occur due to air or tissue blocking acoustic waves from penetrating deeper,
the Gaussian shadow augmentation proposed by Smistad al. (2018) generates and applies two-dimensional

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Original Image
Shadow Image
Augmented Image
X
Figure 6: An example of the Gaussian shadow augmentation the AUL dataset with strength 0.8, and
xx By 0.11.
Original Image
Augmented Image
Figure 7: The speckle reduction augmentation applied to an image from the AUL dataset with Opatiill 1.0 and Ccolor 1.0.
Gaussian shadows with randomly selected parameters. The shadow centre (Na, M4y) is randomly positioned in the image, while its dimensions (az, σy) are sampled uniformly between 0.1 and 0.4 of the image size. This upper limit is lower than the 0.9 used by (Smistad et al., 2018), who originally designed the augmentation for rectangular linear probe images, rather than fan-shaped convex probe images. The shadow strength s is sampled uniformly between 0.25 and 0.8. The Gaussian shadow image G is then calculated as
G(x, y) 8 sxxp (- (-(#-1)) (xr-px) Pz) (y- m1y)
(4)
20
20
Finally, the augmented image I' is generated by the pixel-wise multiplication of G, the ultrasound scan mask S, and the original image I:
((x,,) y) = [(x, u) @ S(x, @ G(a,y).
(5)
An example of Gaussian shadow is shown in Fig.
4.4 Speckle Reduction
Speckle noise is caused by interference between ultrasound waves. The speckle pattern observed in images captured using machines from different vendors often differs due to image enhancement and various filtering methods. As described in Ostvik et al. (2021), we apply bilateral filter with randomly sampled parameter values to reduce the effect of these speckle patterns. We use the bilateral filter implementation from scikit- image (van der Walt et al.] 2014).. The Oppatial and acaler are sampled uniformly from the ranges (0.1,2.0) and (0, I), respectively. An example of this augmentation shown in Fig.
5 Evaluating the Efficacy of Individual Augmentations
We begin by addressing two fundamental questions: (a) how effective is each augmentation when applied individuall?? and (b) how does their effectiveness vary across different domains and tasks?

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Caunder Shudoo
Contrast
None
Zoom
Spock epocke reduction
Randor coop
brightmes
Gaunten
Figure 8: The mean and standard error of the mean average precision (mAP) using each augmentation as well as without augmentation (None) on cach classification task. The shaded area highlights the standard error for the estimate of the mean mAP without data augmentation.
In these experiments, we compare the effectiveness of the top 10 most popular augmentations identified in Scction 2.1 flipping (horizontal and vertical separately), rotation, resizing, random cropping, translation (along the x and y axes), contrast adjustment, brightness adjustment, Gaussian noise, Gamma adjustment; and the four ultrasound-speciffe data, augmentations we previously described: depth attenuation, haze arti- fact addition, Gaussian shadow, and speckle reduction.
5.1 Evaluation Protocol
We evaluated the augmentations on each of the 14 classification and segmentation tasks described in Section In each case, we fine-tuned models using each augmentation in isolation that had been pre-trained on ImageNet. For classification tasks, we used EfficientNetBO0 (Tan & Le 2019) models, while for segmentation tasks we used UNet models [Ronneberger et al. 2015 with Efficient BO backbones. We used Efficient for both sets of tasks because of their popularity in medical imaging and to maintain consistency. We used the smallest BO variants make it feasible to run the evaluations across all tasks. Additional results on a subset of tasks using larger EfficientNet-BA. and Transformer models are presented in Appendix E We used model implementations and pre-trained checkpoints from the MONAT library (The MONAT Consortium 2020).
During training, we applied the augmentations with random strength (where applicable) and with 50 % probability on each image an online fashion. The strength ranges for each augmentation were not tuned specifically for cach task. Instead, a sensible range was chosen that consistent with prior works. The parameters of each augmentation are listed in Appendix B The images were normalized and resized so that the longest edge measured 224 px and padded (if needed) that the final image measured 224 x 224 px before applying data augmentation. The only exception was when using random crop, in which case the images were resized to 256 x 256 px before being cropped to 224 x 224px.
For each task, we performed 30 training runs using different random seeds for each augmentation. We measured performance on the classification tasks using the mean average precision (mAP) and on the seg- mentation tasks using the mean Dice score across all classes. Because the area(s) of interest in the images are small, we omitted the background class when calculating the Dice scores that performance was not
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Published in Transactions on Machine Learning Research (07/2025)
Tock
Augnestataro
AUL Mass
Bartuctly
CAMUS
Party Live
GBCU
MMOTU
POCUS
Mena
UAP
55%
MAP
4%
mAP
AX
IAP
RAP
4%
HAP
4
SAP
35
.3%
Nare
0,32 =
=: (L00)
0.084
0,768 a
0.264
0.005
[UG3] sE 0.003
& 0.012
Plotometne
Speclle reduction
0,000
+4.031
8 0.002
-0.095
0.271
0.270
DUBLS 0L003
-16.413
0.871 2.000)
0U001
0,000
0.756
+4.949
DU547 0L003
0.010
43.06
Ouclastt
14:225
0.50 0.001
0.523
0.500
0.700
1.005
1U522 0.004
12,155
0.012
0.743
13.24
Desth olicouation
0.672
0.55-=
0.002
0.055
0.580
0.534
0.779
13.534
115R0 0.0013
16.839
000012
1.448
13.02
0.659
14.85
01:80 0.003
00.15
0.572
0 0.000
133751
0.750
+0.629
11556
0.007
14.270
0.003
-3.94
+3.49
Caumiin roke
0.618
+2.033
0.000
-0.023
0.500
+2.555
0.754
+3.415
-3454
11G 0.001
-19.191
-3
+3.35
Guuelan dindor
0.655 00.000
+3.715
0.002
-0.355
0.505
-2.078
0.511
+6.950
0.773
161
01545
+ 0.0001
+2.22
0.857 1-0.010
-3.717
+1.08
Rase ariifact
0,758
c: 0.010
37.316
(1961
2: (L002
-0.019
0,001
0.005
+3.510
0,750 0.021
144222
01258
00588:00005
0.055
-0.422
0.571
:: 0.000
47.155
0,543 0.011
-1.770
+2.09
Genreetric
Flis H.
0 0.010
+1.85
0.56 0,002
-0.645
0.007
0.006
+2.550
0.281 a 0.0022
+33060
0.276 0.97
+1.457
[U597 2: 0.001
+4.416
0.867 1: D.005
-2.818
+3.68
Fli V.
0J028 5 0.018
0.544
[1586 2: [U00)
-EL495
0.887
:: 0.000
+0.530
0.768 2 0.028
-0.28
0.254 0197
-1.945
[U52] = 0.001
11965
0,895 2: 0.012
+3.200
-3.58
Randon crop
0,868 = 0.00
10.785
11587 2 [U00]
[0.14
0.206
0.308
13.50)
0,757 = 0.025
13:447
0.797 01077
14.246
[UGIT = 0.003
19,536
0.573
+ EL007
11.788
13.39
Rolate
0.642 0.008
11.63
0.886 [U00]
[1623
0.027
0.006
17.421
0,757 = 0:002
13,488
0.750 0106
189944
[UG22
= 0.001
22.015
0.551 0L007
1.080
13.48
0.636 0.03
+3.50
(118) 01003
0.003
0.011
0.000
+4.727
0.731
+ 0.005
-3.376
0.75)
01006
+2.153
[GG/ 0.003
-18.51
0.85 0.010
-2.70
+1.72
Zoom
0.026 8
+3.301
11022 :: 0.003
-0.55
0.031
0.007
+6.596
00.00 a 0.000
+6.742
0.760 01000
+3.202
0.014 0.001
-15.999
0.500 + 0.009
+3.712
+3.47
Table 1: Mean average precision (mAP) for each augmentation across classification tasks. We report the mean + standard error and relative improvement (A%) for individual tasks and averaged across all tasks.
exaggerated. Before the evaluations, we tuned the learning rate, weight decay, length of training in epochs, and dropout rate per task without data augmentation using Optuna (Akiba et al. 2019). Each model was trained for a minimum of 100 epochs and all model's converged within the allotted training budget, even with data angmentation. More details of this procedure are provided in Appendix
5.2 Classification Results
Fig. g compares the models' mean average precision using each augmentation for each task. These values are also reported in Table ! alongside the percentage improvement, both per-task and across all tasks.
Whether an augmentation increased performance depended on the task. Zoom and random crop were the only augmentations that improved performance on all tasks, increasing performance on average by 5.47 % and 5.39 %, respectively. These were followed by rotate, brightness and speckle reduction that were each effective on 6/7 tasks. At the other end, contrast adjjssiot adjustment and vertical flip were the least effective. They producod the lowest average performance gains (+0.24 % and -0.38 %, respectively) and improved performance on the lowest number of tasks (3/7). Vertical flip was the only augmentation to slightly decrease performance average (-0.38%) %).
There was a clear divide between the elficacy of photometric and geometrie transforms. Despite being detrimental to performance on the POCUS task (-1.09%) rotate produced the highest average performance gains (+5.48 %), followed by zoom (+5.47 %) and random cropping (+5.39 %). In contrast, the most effective photometric augmentation, speckle reduction (+4.12%) only ranked fourth overall.
While not the most effective, the ultrasound--peeeffic augmentations (speckle reduction, depth attemuation, haze artifact and Gaussian shadow) were still reasonably effective. They were beneficial on 4 6 tasks and produced gains of 1.49 %-4.12%% % on average. They were also very effective on some tasks. For example, depth attennation ranked first on the Fatty Liver task, improving performance by 8.74 %.
5.3 Segmentation Results
Figure g compares the models' dice scores using each augmentation for each task. These values are also reported in Table 2 alongside the percentage improvement, both per-task and across all tasks.
In general, the relative improvements on segmentation tasks were much smaller than on the classification tasks. The average improvement of the top performing augmentation (brightness) was only 2.03 %, which would rank only tenth on the classification tasks. It also introduced substantial variability between runs. In fact, in many cases the gains were practically insignificant, with performance increases of < 1 1%. We discuss this more deeply in Section 7. Unlike for the classification tasks, we did not observe a clear divide between the geometric and photometric augmentations. Brightness was the only augmentation to improve
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Published in Transactions on Machine Learning Research (07 (225))
AUL Liver
Mass
CAMUS
MMOTU
0.3015
0.875
0.88
Dice
0.40
Dice
0.3000
Dice
0.870
0.2985
0.00
0.865
Conpuz
Nole
Goussiah shodee
Gamne
Fp verical
zaom
Transdete
Guincin rate
Brgget B-giitanns
Randum cop
Tip vetical
Dupt dosmmaation
byeduum cri0
arthor
Zoum
Gammra
Controz
Soccit rediction Maction
Now
Fie wettel
Danse shetbe
Fip notbbraal None Brggt-en doamnaatonn
recictioon
Hane artfcct
fandam drip
Guuscar te
Rubto
20om
Now Ri varical Canoua
Denn afren in
sight
to fontral
Spccds reiddreaat antllct Zoom
Inemlatt Gaussin
Nandamn
Gauss
Daus
Soccii
Danvee
Daph
Open Kidney
PSPHS
Stanford Thyrold
0.375
0.79
0.82
Dice
Dice
0.78
0.300
0.80
0.345
2:0
Contren
Ho Hald brt Ganma
Nome
Depih ierunation Bragttes
rin hottooaal
Gasss rolse
None
Caunde ched Cottren
RIO hontonta
110 nottcal
Bace artfect
2o0m
Rardom moo cop
Gense miker
speci a redieti
Hatt 50 srt
Cousson 110 neticcll
Conbrat Zoom
Rande a copp Taisiate
huttzonall
Rotake
Spied
Speck
Depin
Figure 9: The mean and standard error of the mean dice score using each augmentation as well as without augmentation (Nonc) on cach segmentation task. The shaded area highlights the standard error for the estimate of the mean dice score without data augmentation.
performance on all tasks, while random crop improved performance on six and Gaussian noise, horizontal flip, rotate, translate, and zoom all improved performance on five tasks.
We also observed marked differences in the effectiveness of augmentations between different tasks on the same dataset. For example, Gaussian noise was moderately effective for the AUL liver segmentation task (+2.77 %), but was very detrimental to performance on the mass segmentation task (-40.41 %). Furthermore, while 11 augmentations were beneficial for liver segmentation only single augmentation was beneficial for liver mass segmentation. These differences highlight the important influence of the task on the efficacy of different augmentations.
5.4 Comparing the Classification and Segmentation Results
The tasks on the AUL., CAMUS, and MMOTU datasets allow us to directly compare the efficacy of augmen- tations across classification and segmentation tasks using the same data. On the AUL tasks, we observed that fewer augmentations were effective the difficulty of the task increased. 13 augmentations improved performance for mass classification compared to 11 for liver segmentation and only one for liver mass seg- mentation.
On the CAMUS tasks, a similar number of augmentations were beneficial for classification (11) and segmen- tation (9). However, some augmentations were only beneficial on one and not the other. Both vertical and horizontal flipping were beneficial for image quality classification, but not for cardiac region segmentation. Perhaps this is because these augmentations create anatomically incorrect images, which is important for region segmentation. On the other hand, brightness, contrast adjustment, depth attenuation and gamma adjustment were all beneficial for region segmentation, but not for image quality classification. Again, per- haps linked with the effect these attributes have on subjective image quality. Finally, on the MMOTU tasks all augmentations were iffective on both tasks with the exception of ical flipping. This was detrimental to classification performance and had little to no effect on segmentation performance.
These results demonstrate that even controlled settings, there is much variation in performance between domains, tasks, and datasets. Which augmentations were or were not useful varied depending on the domain
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Published in Transactions on Machine Learning Research (07/2025)
Tock
Augnestataro
AUL Liver
AUL Mase
CAMIUS
SOOOTU
Ozen Birlnay
PSPRS
Stanfied Thymed
Mona
Dios
31%
Dies
A
Dise
A75
Fixe
Dire
48
Dise
Dixe
355
5%%
Nare
0,85 a
=: 1L012
(,000
0 2
0,007
0.350
0.001
0,773 & 0.002
Plotometre
Speclle reduction
-1.224
0.011
0.200 0.000
0.967
+-0,22)
0.018
-4.452
0.351
40.172
0.001
-3.50
Beiale
+2.77
0.200
0.000
46.047
0.001
+2.00
Ouclast
0,878
0.012
0.292
10.178
0.020
1.731
0.350=
0.002
Deyth ollcouation
0.556
2.316
0.015
7.853
0.230
10.181
0,500
0.000
10.371
0.345
0.001
0,200
11.172
0.002
0.210
+0.099
0.563
10.382
0.000
-0.176
0.350
0.001
1.37m
Coumdan
0.800
+2.77
0.015
0.5T 4:0000
+3.077
=
0.000
0.353
+0.035
0.001
-1
-1.6
Guuelan dindor
0.856 -
+0.137
01501
0.01
-0.525
0.315
-0.291
0.933 4-0.00
+0.557
00.00
0.350
+0.05)
0.779-1 1.0101
+-30005
-3.83
Rase artifact
0,850 2 0.000
10,057
0.689 (L013
-3.856
0,901
0.000
+0.911
0,500 2: 0,001
+0.794
0.827
0,011
-0,402
0.312
0.000
+0.935
0.774 0.007
-2.502
-2.58
Gensuetric
Flis H.
0,866 2: 0.008
+0,02
0.018 2: 0.013
-1.751
0.205
0.000
-0.208
0.80 0.000
+0.411
0.536
0,010
+0.218
0.851=000
+0.377
0.290 (10015
+-1.00
-3.14
Fli V.
0,806 2 0.002
41.195
0.014 IUBL
-8.355
0.207 2,000
-0.005
0,864 2 0:0000
10.008
0 0.018
-0,872
0.852 00000
10.46
0,781 2:2:2:000 [L01]
+0.485
-1.30
Randon crop
0.014 0.001
18222
0-684 [LBL3
4.904
0.201 0100
18,98
0,578 2 0.000
12,600
0.631
= 0.000
10.85
0.353::00 00000
10.225
0.787
2 [L001
11.00
13.46
Rolate
0.828 0.008
11:501
[.50] 2 (L012
1.213
+ 01000
18.66
0-000
13.36
0.806 6.017
3865
0.353=100 00000
10,97
0,701 0.002
11.54
0.28
Tramllee
0.859 0.004
+1.025
1.505 0.012
-0.38
0.300
0:011-100 01000
+0.588
0.571 +
+0.947
0.808
0.014
-2615
0.353 0.000
+0.93
0.78 0.003
+3.872
+3.37
Zoomn
0.896 5 0.006
+1.251
0.501
:: 0.013
-0.61
0.300
01000
+0/07
0.500 0:0000
+0.407
0.807 = 0.012
-2.705
0.353 00000
+0.10
0.781 2: 0.003
+3.006
-3.16
Table 2: Mean dice score for each augmentation across segmentation tasks. We report the mean + slandard error and relative improvement ()) for individual tasks and averaged across all tasks.
(o.g.; fotal vs. cardiac ultrasound), task type (classification vs. segmentation) and the particular task being performed. Further experiments using larger EfficientNet and transformer models (Appendix E show that the choice of model also impacts the efficacy of different augmentations. In the following section, we investigate the possibility of extracting greater, more consistent performance gains through more diverse augmentation.
6 Applying Multiple Augmentations
In the previous section, we demonstrated that individual data augmentations can improve model generaliza- tion on ultrasound image analysis tasks. However, in practice, we typically combine multiple augmentations to increase data diversity in different ways. This raises the question of how we should combine them. The AutoAugment family of algorithms (Cubuk et al.) 2019: Lim et al., 2019 HHtaya et al. 2019] pio- neered automated augmentation strategy optimization, but these methods are computationally expensive and data-intensive. This has limited their adoption beyond using the strategies discovered for natural im- age benchmarks, such as CIFAR-10 and TmageNett in the original articles. Simpler alternatives such as RandAugment {Cubuk et al.) 2020) and TrivialAugment (Mäller & Hutter 2021) have since demonstrated comparable performance on these tasks without costly optimization. However, these techniques remain un- derutilized in medical imaging. Extending our previous results, we investigate whether TuinenlAugient is effective for ultrasound image analysis, and examine how the inclusion of different augmentations affects performance.
6.1 Evaluation Protocol
TrivialAugment transforms each image by randomly selecting two augmentations with replacement (including the possibility of no augmentation) from predefined set, and applies them sequentially. To evaluate how performance changes as we expand beyond the individually most effective augmentations, we trained separate models using PrivialAugment with the top-N most effective augmentations for each task, where N ranged from 2 to 14. These augmentation sets correspond to reading right-to-left across the sub-figures in figures g and g All other experimental conditions matched those used in our individual augmentation evaluations
Section 1
6.2 Classification Results
Fig. 10 displays the trends in performance as the set of augmentations is expanded for each classification (ask. The percentage improvements, both per-task and across all tasks, are reported in Table 4 of Appendix [D] Across all tasks, the best TrivialAugment configuration outperformed both the no augmentation and single best augmentation baselines. The best configurations produced a further 0.38 %-9.02 % improvement in mean average precision over the single best augmentation and 0.99 9430.220 % improvement over augmentation.
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Published in Transactions on Machine Learning Research (07/2025)
AUL
Butterfly
CAMUS
Fatty Liver
0.76
0.992
0.85
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1011121314
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PAP
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0.55
0.84
5.6 7 7.8 Number Augmentations
1011121314
0.1. 91011121314
2 7 1011121514 Number of Augmentations
Number of Augmentations
Figure 10: The mean and standard error of the mean average precision (mAP) using the Top-N augmenta- tions on the classification tasks. shows the performance without data augmentation. and show the addition of effective, ineffective and harmful augmentations, respectively.
For cach task, performance initially increased as augmentations were added beforc declining. On the CAMUS, Butterfly, and POCUS lasks, the decline in performance coincided with the addition of individually harmful augmentations. However, on the remaining tasks performance began declining well before incorporating harmful augmentations. We discuss possible explanations for this Section 7.3
Finally, Tugment still surpassed the no augmentation baseline across most tasks using all 14 augmen- tations, i.e., without requiring curated augmentation sets. The only exceptions were the Fatty Liver and POCUS tasks. This shows we can still achieve performance gains without investing resources in evaluating and optimizing individual augmentations and the augmentation set.
6.3 Segmentation Results
F shows the trends in performance as the set of augmentations is expanded for cach segmentation task. The percentage improvements, both per-task and across all tasks, are reported in Table 5 of Appendix As observed when evaluating the individual augmentations, the performance improvements are relatively modest in comparison to the gains seen in the classification tasks. While the best configurations produced 0.38 % 9.02% % improvements in the dice scores over no augmentation, the gains over the single best augmentation were between -0.01 % and 6.24 %, with the single best augmentation slightly out performinnng Auiveel on the AUL Mass and PSFHS tasks.
Across all tasks, observe the same pattern as the classification tasks. Performance initially improves the set of augmentations grows before declining. This pattern also observed when repeating our experiments using larger EfficientNect and transformer models (Appendix E The results on the CAMUS task are an exception where performance continues to increase, with diminishing returns, as augmentations are added even augmentations that reduced performance when evaluated individually. As observed on the classification tasks, we can link the declines on the AUL Liver, AUL Mass, and Open Kidney tasks with the introduction of harmful augmentations, With the exception of the AUL Mass segmentation task, TrivialAugment again consistently out performeed the augmentation baseline even without. careful augmentation selection.
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Published in Transactions on Machine Learning Research (07/2025)
AUL
Mass
CAMUS
MMOTU
0.882
0.304
0.915
0.50
0.876
0.302
Dice
0.900
0.45
0.670
0.300
0.885
0.40
0.964
1 6.7. 91013121314
0.1
1033121314 Number of Augmenttatins
2 10111223344
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Number of Augmeniattions
Number of Augrenttttions
Numbee of Augmentations
Open Kidney
PSPHS
Stanford Thyroid
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0.38
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0.86
0.37
Dice
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0.35
7 91011121314
91011121314
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The mean and standard error of the dice score using the Top-N augmentations on the segmentation tasks. shows the performance without data augmentation. and show the addition of effective, ineffective and harmful augmentations, respectively.
Figure 11:
7 Discussion
Our experiments reveal both the potential and limitations of data augmentation for ultrasound analysis. We now examine the practical implications of our findings, present recommendations, and acknowledge important limitations of our study.
7.1 The Benefits of Augmentation for Classification and Segmentation Tasks
Despite low uptake, our experiments demonstrate that traditional domainn-nndependent data augmentations are effective for ultrasound images and strongly support the use of data augmentation when training models for ultrasound image analysis tasks. This should dispel any notions that the generated images are unrealistic and therefore not useful. However, the improvements in the metrics are generally larger for classification tasks than for segmentation lasks, even when controlling for the dalaset. This suggests several possibilities: there may be lower ceiling for the effectiveness of data augmentation in segmentation, these tasks might be more sensitive to the strength of augmentation, or there is still room for developing more effective augmentations for segmentation lasks in medical imaging. Despite this, the small gains observed in some tasks should not be dismissed as insignificant. Ultimately, the practical significance of any gains depends on the specific use case, as performance is measured differently for different tasks and tolerances for error can vary.
7.2 Considerations for Domain and Modality-Speciffi Augmentations
Among the ultrasound-speciffi augmentations tested in our experiments, none consistently outperformed all traditional augmentations across tasks. Therefore, researchers should carefully evaluate whether these augmentations suffice before investing significant time and resources into developing custom augmentations for specific domains or tasks. When proposing new augmentations, their performance should be compared against existing augmentations to identify situations where they are beneficial.
Nevertheless, the four ultrasound-specffic augmentations we tested still produced performance gains in most settings, and we demonstrated their broader effectiveness beyond the initial cardiac and nerve/bloot vessel ultrasound contexts. Additionally, we hope our implementations lower the barrier to entry for using these augmentations and encourage further testing of their practical utility.
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Published in Transactions on Machine Learning Research (07/2025)
While we did not formally benchmark the computational cost of each augmentation, only speckle reduction increased the training time (by 1.5-2x) and only for the smaller models. This is despite the fact that the standard augmentations benefit from highly optimized implementations and our implementations weren's 't optimized for efficiency. When training the larger GPU-bottlenccked models (Appendix E) this difference was eradicated. Moreover, when using TrivialAugment the computational overhead of speckle reduction was negligible, even with small models, since each augmentation selected relatively infrequently. These findings show that these ullrasound-specffee techniques can be included without substantial computationnl cost in practice.
7.3 Considerations for TrivialAugment
The original analyses of TrivialAugment by Müller & Hutter (2021) on the CIFAR-10 dataset found that performance increased and then plateaued as the augmentation set size grew. However, our results across a wide range of ultrasound tasks demonstrate that careful selection of augmentations is crucial for maximizing performance in this setting. Not only did some augmentations harm performance, but for many tasks, performance declined after certain point as more augmentations were added even if they were effective individually. This decline might occur because each augmentation is applied less frequently augmentations grows, diluting the impact of more effective augmentations. A good approach is to evaluated each augmentation individually (i.c., training single model per augmentation) to identify the most effective augmentations for the task. These can then be used with TrivialAugment to unlock better performance without having to invest the extensive effort required probabilities, strengths and sequences) either manually or via automated search. Even so, in 11 out of 14 tasks, blindly applying TrivialAngment without paring down the set of augmentations still led to improved performance and researchers should be encouraged by the fact that substantial gains can be achieved with limited tuning.
the set of
design and tune a strategy (i.e., augmentation
7.4 Limitations and Path Forward
In this work, we focused more on the individual offectiveness of different augmentations than different strategies for applying multiple augmentations. Despite that, we have remonstrated the importance of removing ineffective augmentations from the augmentation set when using TrivialAugment on ultrasound images to achieve peak performance. The extent to which these results hold for different model architectures and training algorithms is likely to vary, but we expect the general conclusions to remain the same. Moreover, we provide strong evidence for the use of data augmentation training ultrasound analysis models and hope that researchers reconsider the omission of data augmentation in future work. Finally, we hope that our findings and methodology motivate and inform follow-up studies on other medical imaging modalities.
8 Conclusions
In this work we addressed a gap in our knowledge of effective data augmentation for ultrasound images, conducting the most rigorous analysis to date of commonly used data augmentations for ultrasound image analysis and comparing them against augmentations specifically designed for ultrasound scans. The results demonstrate that strong performance gains are possible using data augmentation, both when applied indi- vidually and when combined using Augmeet. As part of our contributions, we created standardized benchmark for ultrasound image analysis tasks, reducing the effort required to evaluate ultrasound image analysis methods and allowing researchers to test their methods more broadly across wider range of tasks and domains. We hope that these tools and findings encourage rescarchers and practitioners to use data augmentation more often and provide a blueprint for future investigations into the effectiveness of other data augmentation techniques or imaging modalities in medical imaging.
Broader Impact Statement
This work aims to improve the reliability and accessibility of automated medical image analysis, particu- larly in settings where large datasets are difficult to obtain. While our techniques could enhance diagnostic
16

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Published in Transactions on Machine Learning Research (07/2025)
capabilities, they should complement rather than replace human oversight in climical decision-making. We emphasize the importance of thorough validation across diverse patient populations to ensure that perfor- mance improvements translate equitably to different demographics and healthcare contexts.
Acknowledgements
This research was enabled in part by support provided by Calcul Québec (caloulquebec.ca) and the Digital Research Alliance of Canada (alliancecan.ca). It was also supported through funding from Canadian Institute for Advanced Research (cifar.ca) and the Natural Sciences and Engineering Rescarch Council of Canada
(nserc-orsinggg.caa).
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A Examples of Generated Ultrasound Scan Masks
MMOTU
Star ford Thyrodd
Open Kilney
AUL
Buiterlly
CAMUS
Fatly Liver
GBCU
POCUS
PSFHS
Figure 12: Examples of the ultrasound scan masks generated for cach datasct in UltraBench.
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B
Augmentation Parameters
Table
B contains the parameters for cach of the augmentations tested in our experiment.
Augmentation
Albumentations Class
Parameters
Bilateral filter
Ospatial E (0.05, 1.0)
Ospatial E (0.05, 1.0)
window size =
Brightness
RanddonBrigghttuesSoootraa BandoonlrighhtreesCoonzzat
brightness _imit E (-0.2,
Contrast
contrast limit E (-0.2, 0.2)
Depth attenuation
attennation __tee_attee- (0.0,3.0)
max attemuation 0.0
Flip vertical
VerticalFlip
N/A
Flip horizontal
HorizontalFlip
N/A
Gamma
RandomGamma
gamma limit E (80, 120)
Gaussian noise
GaussNoise
var_ par_limit- = 0.0225
0.0
per orr_chanel = False
noise scalecllee..... factor strength E (0.25, 0.8)
Gaussian shadow
o, (0.01, 0.2)
By (0.01, 0.2)
Haze artifact
radius E (0.05, 0.95)
e (0.0,0.1
Random crop
RandomCrop
width 224
height 224
Rotate
Rotate
limit E (-30, 30)
border mode
value = 0
Translate
ShiftScaleRotate
shift limit (-0.0625, 0.0625)
interpolation =
border mode value = 0
Zoom
ShiftScaleRotate
scale limit E (-0.1.0.1)
interpolation =
border mode
value
Table 3: The scttings for each the augmentations tested in our experiments. We used the implementations from the Albumentations library where possible.
C Hyperparameter Tuning Procedure
To account for differences between tasks, in particular the number of examples each dataset, we optimized the key regularization hyperparameters (training length in epochs, learning rate, dropout rates, and weight decay values) per task using the Optuna hyperparameter tuning framework. For each lask, we performed 100 trials without using any data augmentation using the Tree-structured Parzen Estimator algorithm with the default parameter values. The values for the number of epochs were sampled from the set (50, 100, 200). the learning rate sampled from the log domain between (10-6 10-3), the dropout rates between (0.0, 0.5), and the weight decay from the log domain between (10-1,10-2), The optimized values for each hyperparameter are listed in the task configuration files in our accompanying source code.
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D TrivialAugment Results
Taok
Strategy
AUT. Mase
Butterll
CAMUS
Visty Liver
GBCU
MMOTU
POCUS
Meen
DAP
5%%
None
0,632 0.000
0.005
0.864
0.001
Ti--W
0 01000
19,504
0.587
11001
[B614
0.006
17.429
187388
0.716
14.323
0.650
0.000
122,015
0.857 +00000
17.90
0.715 01000
114.791
0.865
0.000
0.006
+4.740
13015
0.535
10005
15,000
0.G50
0.000
123.850
01850
0.012
+2.94
+9.27
0.740 (1000)
+17.218
+5.001
+1.456
0.036
0,855
0.877
+1.350
0.747 11.00
-18.200
0.903
+5.635
-5.351
0.505
+3.317
0.094
4:30.220
0.688 0.011
110.20
0.745 01000
15.201
18,720
123.454
0.857 0.007
10.28
-19.376
+7.015
0.749
-18.16
+0.217
-7.07
+27:416
0.655 1-0.01
0.663
+10.218
+0.03
+27.125
0.800
+3.055
+9.35
+7.009
+9.77
677
0.731
15,6%
0.967
10029
0664+
130.225
0.770 1.121
11613
0.510
15.925
01681 0:00013
737
0.878 0.0011
11:622
19.00
0.731 0000
0.065
0.4
+10.199
0.7.4
0.800
+4.307
2.00001
0.88 0-0.013
0.721 U00G
+3.51
0.713
-0.13
11005
0.67V
03848 0.00011
-1.77
0,711 = 0.00G
16.794
0.067
0.001
00635
0,834
0.004
$5833
5.022
0,515 0.005
15.545
0.676 2 0.003
125 803
0.863 0.013
00.13
47.84
Table 4: Mean average precision (mAP) for TrivialAngment with each set of the Top-N augmentations across classification tasks. We report the mcan + standard error and relative improvement (A%) for individual tasks and averaged across all lasks.
Stastogy
AUL Lives
Mus
CAMUS
MIMOTU
Kilacy
PSPHS
Stondord Thyrold
Moan
.%%
Dine
Dioe
Dice
4%
Dice
43%
Dree
5%%
Diee
4%
Nica
0,585 0.006
0.907
0.012
0,280 a: 0.000
0.863 &: 0.001
0,530
8 0.007
0:350 a: 0.001
0.779 e: 0.000
Tip. NV
0.914 >: 0.001
+32252
0.627
0.010
+5.000
0.301 4: 4:0.00
-0.957
0.888 5: 0.000
+1.620
0,845
0.008
-1.801
0.368 4: 0.015
+5.004
0.701
5.0.002
4-1.51
+2.57
0.000
+4.188
11008
+4.222
0.3012
02811 0.000
+/2001
0
0.000
-0.500
03333
+11.03
01300
+-2.768
+1.05
0.000
10.926
0.012 0.000
01250 0.001
+1.067
0.363 0.000
0.500
+.2.288
+2.74
0,500 0.000
22935
0.617 10011
0.303 0.020
11.380
112500 0.001
11:967
6.241
[.366
14.318
0,500
00.003
12.046
13:25
0,022 0.000
-4.184
0.511
000000
0.880
+1.915
0.560
+3.454
+5:20
0,922
+4.19
0.303-100010
-1.314
01811 00000
+1.975
-0.703
0330 0:005
+33.821
0
+4.1226
+3.05
0,524
-4.330
+0.836
0.314:100000 0.000
-16617
+1.705
-5.823
0.364 0.010
+0.945
0,811
+4..244
+8.33
0,921 0.000
-1.355
0.568 0012
+/1190 199
0.305
0.030
-1.391
0,873 0.001
+1.789
0.508
0.005
-1641
0.31
000011
+4.155
0.807
0.0013
+5.009
-2.1
0,925 0.000
-4.470
0.511
0012
+0.700
0.361 0.000
-1.721
0,800
0.000
+1.000
0.506
0.00
-1.330
0.355 0.011
+4..61
0,810
0.000
+4.002
-301
10
0,915 0.100
-4.476
01508-1
à: 00012
+0.250
0.304:200000
-1.796
01881 5: 110000
+1.991
0,960
0 0.000
-36636
01364
3: 01000
+33-963
01301
5: 0.300
+-3.9/8
+2.76
11
0,522 2 0,000
-4.157
0.456 1 D.012
-2.908
0.314 0.000
-1.743
0,880 2: 0.000
+1.905
0.567 5 0.011
-8.258
0.360 1: 0.006
+2.76L
0.565
2: 0.302
+3.220
-2.15
19
0.523 0.000
-1 196
0-900
0013
165
0.301 + 0.000
-1 1.7791
0.90
+ 0.001
+1 .965
0.505 0.000
-1 150
0.365
+ 0.010
+3.307
01200
0.002
+2.875
+1.10
18
0.522 8 0.000
-55
0-455
1:555:0000 0011
-10.65
0.911 0.000
-1 A02
0.80
E: 0.001
+1.911
0.905
= 0.005
-139
0.361
+: 0.008
+3.347
0.750
0.002
+2.616
-1.07
14
0,9233 8 0,000
-4.271
0380 1UT1
-33256
0,384 4
+1.554
02291 5: 0.000
+1.955
0,553 2 0.008
-2757
0.350 3: 0.006
+22.48
0.716 0,202
+2.181
-1.09
Table 5: Mcan dice score for TrivialAugment with each set of the Top-N augmentations across segmentation
tasks. We report the mean + standard error and relative improvement (A%) for individual tasks and
averaged across all tasks.
E Evaluations with Different Model Sizes and Architectures
To assess the influence of model size and architecture on augmentation effectiveness, we repeated our ex- periments using larger Efficient.et backbones, and Mix Transformer (MiT) and SegFormer models {Xie et al. 2021). ). We tested the individual augmentations and Arimeal on a subset of classification and segmentation tasks on three datasets: a small dataset (AUL Mass), a medium a large datasct (CAMUS). For each task, we trained the models for 200 epochs and optimizod the lcarning rate and weight decay per model on the validation set using a grid search over seven logarithmically spaced learning rates between 10 and 10-1 and seven logarithmically spaced weight decays between 10 and 10-3, All models reached convergence within 200 epochs.
dataset (MMOTU) and
E.1 Individual Augmentations
Model size and architecture strongly influence augmentation rankings, just like the lask and domain/ domain/dataset /dataset (Section 5). As shown in figures 13 and 14. the performance gains for each augmentation vary considerably between models, even when the pre-training data (ImageNet) is the same. This variation suggests that different models possess different levels of robustness to various characteristics, such as changes in brightness, which makes augmentations that perturb these characteristics less effective. Consequently, these findings
23

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Published in Transactions on Machine Learning Research (07 (025)
Mass (E#hicenttiet-85)
CAMUS (EficientNeet-B5)
MMOTU (Ef1ideenNeet855)
0.63
0.750
0.64
mAP
0.725
mAp
0.62
mAP
0.60
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CRinsinn rate
Special redaction
Gamma
Conma
Pardomm crip
Now
Trandate
Dogge taa Zoom
Daph anamaation
Fb verrical
1oz arttrect
Randam
Brigit
Contrna
CaIL shetow
Nove
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Flo VESICOl
Maoe aftffet
Dupoh doumaation
Dighh Nore
Gamno
Conteo
Dauss shaddw
Flo veriial
Speciss reductioon
Gauscian reso
Trensate
Zoun
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Fie
AUL Mass (MIT-B2)
CAMUS (MIT-B2)
MMOTU (MIT-92)
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mAp
map
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Contrans
Nome
Brighties
Zoom
Tits honsomtal
cousian soniunnnnocoon shodiwe
Gauss an milze
Random cop
Hare art erfeect
Spaedo rxduction
Gamma
Rin hotteont
Contratt
Torsate
Speck reduction
Zoom
Dupsh attoruationn
Hace srriect
Gauss an mole
None
Dosth Contraet
Hare ortiect
Brightnes
Caunder staddo
Spaci reduction
Gauss an miloe
Fila hottzoaal
Them
Randon
Depih
Figure 13: The mean and standard error of the mean average precision (mAP) using each augmentation as well as without augmentation (Nonc) for different models on the AUL Mass, MMOTU and CAMUS classification lasks. The shaded area highlights the standard error for the estimate of the mean mAP without data augmentation.
AUL Mass (EfficienNNett85 UNet)
CAMUS @BfficienNeet85 UNet)
MMOTU (eficientNet-85 UNet)
0.3048
0.882
Dice
Dice
Die
0.876
0,3032
0.870
0.72
0.3024
0.864
Gammo
Nore
Canrrst
Fi teltzortal
Goussinn cuusiinztodww shadow
Thindaree
SoccMe reduction
Fip veriial
Haze arttact
Zoom
Guusiiun 1e re
bightrees
Rotata
Random
Rip Nove
Fi to tonttnaal
Daph acuruation
Specil reductionn
Gamma
Gaussion to
Conto
srgttnncs
Rotate
Zoom
Tronslss
Canrzut
Guussan Suussanstadde staddd
Gamma
Spockd reduction
Dishh accouationn
Zoumn
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Fio to dontat
Transiac
Random crsp
Rotate
Gussius
Gausinn
Haze
Daph
AUL Mass (SegtormenB2)
CAMUS (Segtomer-82)
MMOTU (Segtormer62)
0.885
0.80
Dice
Dice
Dice
0.880
0.78
0.72
0.875
0.64
Nine
Conrozt
Gamma
brightees
Haze bett
Diph site contaattoon
nortoornal
Zoom
Trrnistt
Random cop
Rio vertcal
Random 2000
Haze Ort
Spock Spokk reduciionn
Gomma
Brightnas
shodow
Zoom
Fil tortooaal
susste
Suusianshoddn shodow
cuttiall
Dapth site rontaattion
Commest
Gauss Gasss nalse
Brightmes
Spack resuutioon
Haze bet
Rib nortonall
Random crp
Gaussán
Coussan
Figure 14: The mean and standard error of the mean dice score using each augmentation as well as without augmentation (Nonc) for different models on the AUL Mass, MMOTU and CAMUS segmentation tasks. The shaded area highlights the standard error for the estimate of the mean dice score without data augmentation.
underscore the need to tailor data augmentation strategics to specific model architectures much like tuning other hyperparametens, such the learning rate.
24

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Published in Transactions on Machine Learning Research (07 (025)
E.2 TrivialAugment
Our analysis reveals consistent trends in the performance of Trivial TrivialAugment across different models, despite variations in individual augmentation efficacy, As illustrated in figures 15 and I6 we classified augmentations into three categories: effective, incffective, and harmful. This classification was based on whether the mean performance of each augmentation lies above, within, or below the standard error of the baseline (no augmentation). The results show the same performance pattern as our experiments across all the tasks with smaller models: utilizing only the most effective augmentations yields the highest model performance. Progressively introducing less effective or potentially harmful augmentations leads to gradual decline in overall performance.
AUL Mess (CffcieenNee-8S5)
CAMUS (EffccientNet-05)
MMOTU (CfficieetNet--55)
0.66
0.775
0.750
0.64
MAP
0.725
0.62
0.56
0.700
Number of Augmentations
Number Augmentations
Number of Aug mentations
AUL Mass (MIT-882)
CAMUS (MIT-82)
MMOTU (MIT-82)
0.66
0.775
0.750
0.64
mAP
mWP
0.725
0.62
0.56
0.700
12
Number of Augmentation
Number Augmentations
Number of menttataioons
The mean and standard error of the mean average precision (mAP) using the Top-N augmen- tations for different models on the AUL Mass, MMOTU and CAMUS classification tasks, shows the performance without data augmentation. augmentations, respectively.
Figure 15:
and show the addition of effective, ineffective and harmful
25

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Published in Transactions on Machine Learning Research (07/2025)
[EficientNee B5 UNet)
[EMiiiintiettB UNet)
(Efficientiie-85
0.89
0.80
Dice
0.300
0.88
0.76
0.303
0.87
0.72
9
10
31 12
14
2
10
11
12
14
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7
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Number of Augmentations
Number of Augmentations
Number of Augmentations
(Segformer B2)
(Seafomen-822
(Segformen82)
0.888
Dice
Dice
0.78
0.64
0.872
1 2
10 21 12 Number of Augmentations
2
7 10 11 32
14
2
12
Number of Augmentations
Number of Augmentations
Figure 16: models in the AUL Mass, MMOTU and CAMUS segmentation tasks. shows the performance without data augmentation. and show the addition of effective, ineffective and harmful augmentations, respectively.
The mean and standard error of the dice score using the Top-N augmentations for different
26


Reviews:
[{"review_id": "l9M6kEMPJ8", "reviewer": "Reviewer_Kfqu", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The paper performs a well-organized and relatively comprehensive comparative study, presenting findings interesting to the field of ultrasound, and potentially the medical imaging community in general.\n2. A good literature review on ultrasound-specific augmentation methods.\n3. There are abundant details on the datasets, including the number of subjects, the dimension of images, etc.\n4. Good findings about harmful augmentations, which seems less studied in literature.\n\n\nWeaknesses:\n1. The results and claims related to TrivialAugment could be better explained and supported.\n2. The comparison between classification and segmentation results could be better justified.\n3. Some details are missing or unclear.\nPlease see detailed comments below.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper assesses a wide range of data augmentation techniques on ultrasound images, across a wide range of datasets, body parts, and problems in classification and segmentation. It also presents a benchmark for evaluating augmentation methods for ultrasound. Results are presented comparing the performance gains achieved by each individual augmentation and using a varying number of multiple augmentations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Below are my questions and comments. Please consider addressing them and changing the paper if helpful.\n\n\"we have created a benchmark of 14 different convex ultrasound image classification\"\nWhat does convex mean here?\n\nIn Figure 2, does each example stand for a 2D image, a 3D volume, or a subject?\n\nIn many of the datasets, one subject has multiple images. When performing classification task, did the authors predict the labels from a single image, or all the images from a subject? If the label is patient-level, e.g., normal/benign/malignant, then predicting from a single image may not be reasonable, as the pathology of interest may only exist in just a few slices among all images of a subject.\n\n\"For each task, we performed 30 independent repetitions of each augmentation.\"\nCould the authors elaborate on this? What does \"repetitions\" mean here?\n\nCould the authors elaborate a bit on \"Brightness\" augmentation? What does it do? Can it be replaced by normalization (i.e., normalizing the brightness of images to a common range)?\n\n\"In general, augmentation was less effective for segmentation than classification.\"\nI'm not sure if it is valid to compare performance gains between classification and segmentation. They are different tasks, use different metrics, and has different baseline (no-augmentation) performances. Although the authors compare percentage change in metric, I'm not sure if the same percentage change indicates the same level of improvement in terms of e.g., impact on clinical practice.\n\n\n\"TrivialAugment transforms each image by randomly selecting two augmentations with replacement (including the possibility of no augmentation) from a predefined set, and applies them sequentially.\"\n1. What's the different between TrivialAugment and a baseline multi-augmentation strategy? Is it that TrivialAugment selects two at a time and the baseline potentially uses all the augmentations on each image?\n2. Did the authors apply TrivialAugment for all numbers of augmentations or just when using all 14 augmentations?\n\n\n\"..., we find that applying a diverse set of augmentations using TrivialAugment (Müller & Hutter, 2021) achieves substantial performance gains with limited tuning of the augmentation set.\"\nHave the authors compared TrivialAugment with a baseline method for multiple augmentations?\n\n\n\"Evaluating each augmentation individually and applying TrivialAugment with the top three to five augmentations often maximized this trade-off and avoids detrimental augmentations. Adopting this approach is a good compromise between time consuming manual tuning and resource intensive automated optimization.\"\nBy \"Evaluating each augmentation individually\", did the authors mean training a model for each augmentation? What's the difference between this and \"time consuming manual tuning\"?", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "fhEcRwY3Nn", "reviewer": "Reviewer_LQtm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**:\n\n1. Conducts a rigorous analysis of the different types of augmentations used on ultrasound images.\n2. Provides an in-depth comparison of the effect of different types of augmentations on performance.\n3. The paper also covers a wide range of anatomical domains.\n\n**Weakness**:\n\n1. In Figure 8, it is clearly visible that the four augmentations that the authors claim to have not been used beyond their original works are not actually that good in improving performance over the wide range of tasks that the authors have selected. \n\nFor example, Randon Crop and Zoom figures in the top half for all the datasets, while none of the four, like Depth attenuation, Speckle reduction, Gaussian shadow, Haze artifact, does not. \n\nHow do the authors justify the choice of the augmentation in Section 4?\n\n2. In continuation with the above point, does the performance improvement obtained using the augmentations in Section 4 provide a significant performance-complexity trade-off? The complexity in the previous sentence refers to the computational complexity of applying any augmentation mentioned in Section 4. \n\n3. In most cases in Figure 8, we can see that the augmentations in Section 4 provide less performance improvement over several trivial augmentations like Random crop and zoom. Thus, I wonder if it would provide more insight if the TrivialAugment could be applied on the remaining 10 augmentations (without the augmentations mentioned in Section 4) and then the performance is analysed.\n\nIn other words, the authors can consider this as a question, whether combining the augmentations mentioned in Section 4 with the rest 10 truly improves performance.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper provides an exhaustive account of the different types of augmentations that have been applied for ultrasound images over the years. Furthermore, this paper also accounts for the different types of ultrasound images available and the augmentations optimal for them. It also studies the effectiveness of the different augmentations for different kinds of tasks as well.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. In connection with Weakness number 2, the authors can provide a comparison of the time required for the data processing pipeline with and without the augmentations in Sec 4.\n\n2. If possible within the given period for revision, the authors could analyse the question in Weakness number 3.", "broader_impact_concerns": "Addressed in the paper.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ndXxdOFVt2", "reviewer": "Reviewer_HpXm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "\\+ The paper presents an interesting analysis of a topic that is well known for natural images, but not so much for specific domains, like in this case ultrasound images.\n\n\\+ To further motivate the importance of this work, authors show that for ultrasound images in most papers data augmentation is not really used, even if, with their results they show that it could effectively improve performance\n\n\\+ The paper proposes some simple strategies to boost performance on ultrasound images for image classification and segmentation without a large exploration of hyper-parameters\n\n\\- Results are validated only for EfficientNetB0. This is quite limited for a study of data augmentation. What is the effect of Data augmentation when changing the size of the model? The CNN architecture (e.g. ResNet, or ConvNext)? What about ViT? Would they perform similarly? I think those are common architectures and state-of-the art methods that cannot be overlooked. \n\n\\- It seems that the hyper-parameters of each model are optimized without data-augmentation (end of section 5.2). In my opinion, the hyper-parameters should be tuned with the augmentations. Consider for instance the number of training epochs. It is well-known that when adding DA the training would require more epochs to reach optimal performance.\n\n\\- The part of combinations of different augmentations could be explored more. Here authors consider using the simple TrivialAugment with the top-k best single augmentations. Without exploring other methods for combining the augmentations, did you consider that the combination of some augmentations can have some side effects. For instance, two augmentations could be incompatible and using them together could reduce the performance.\n\n\\- Some important details are missing. For instance, what kind of pre-training is used in the experiments? What is their effect? The trend would remain the same with a different or no pre-training?\n\n\\- At the bottom of page 4, authors talk about \"convex\" ultrasound images, without really clarifying the meaning of convex in this context.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper evaluates the importance of data augmentation (DA) for ultrasound images. For doing that, the authors first introduce a new benchmark, which is the combination of several datasets of ultrasound images for image classification and semantic segmentation. Evaluations are performed for the most common general data augmentation as well as some more ultrasound specific augmentations. In addition also the combination of multiple augmentations is used through TrivialAumgent. Results show that general image augmentations are effective also on this domain and should be adopted by the community. In addition also modality specific augmentations could help. In addition, augmentations seems to be much more effective for image classification than segmentation. Finally, the combination of multiple augmentations with TrivialAugment can further improve performance.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "(critical) I would like to see the effect of DA on different models such as other CNN with different amount of parameters and architecture and ViTs. I would suggest to reduce the number of repetitions (to 3 or 5 for instance), but testing different models.\n\n(critical) Although more expensive, the hyper-parameter tuning should be done for each specific augmentation to have optimal results. I would like to see results with these setting and compare with what proposed of tuning hyper-parms only on the training without DA.\n\n(optional) It would be interesting to explore how the combination of multiple augmentation works. If you greedly select the best K augmentations is this optimal or there might be augmentations that are not compatible?\n\n(optimal) It would be interesting to see what is the effect of pre-training in the proposed results.", "broader_impact_concerns": "I am ok with that.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)