Learning to Zoom with Anatomical Relations for Medical Structure Detection
Authors: Bin Pu
Liwen Wang
Xingbo Dong
Xingguo Lv
Zhe Jin
Venue: NeurIPS 2025 poster ()
Keywords: Zoom Pattern;Anatomical Relation;Medical Structure Detection
Primary area: machine_learning_for_sciences

Abstract: Accurate anatomical structure detection is a critical preliminary step for diagnosing diseases characterized by structural abnormalities. In clinical practice, medical experts frequently adjust the zoom level of medical images to obtain comprehensive views for diagnosis. This common interaction results in significant variations in the apparent scale of anatomical structures across different images or fields of view. However, the information embedded in these zoom-induced scale changes is often overlooked by existing detection algorithms. 
In addition, human organs possess a priori, fixed topological knowledge. To overcome this limitation, we propose ZR-DETR, a zoom-aware probabilistic framework tailored for medical object detection. ZR-DETR uniquely incorporates scale-sensitive zoom embeddings, anatomical relation constraints, and a Gaussian Process-based detection head. This architecture enables the framework to jointly model semantic context, enforce anatomical plausibility, and quantify detection uncertainty. Empirical validation across three diverse medical imaging benchmarks demonstrates that ZR-DETR consistently outperforms strong baselines in both single-domain and unsupervised domain adaptation scenarios.

Learning to Zoom with Anatomical Relations for Medical Structure Detection
Bin Pu', Liwen Wang", Xingbo Dong", Xingguo Lv', Zhe Jin
'Hunan University "Anhui University
pubinChnu. edu. cn, liwenwang@stu ahu. edu. cn xingbo. dong@ahu. aeiaahuu.ed...cm en, lvxg@hnu. edu. cn, zhejinGahu, edu,
Abstract
Accurate anatomical structure detection is critical preliminary step for diagnosing diseases characterized by structural abnormalities. In clinical practice, medical experts frequently adjust the zoom level of medical images obtain comprehensive views for diagnosis. This common interaction results in significant variations in the apparent scale of anatomical structures across différent images fields of view. However, the information embedded in these zoom-induced scale changes is often overlooked by existing detection algorithms. In addition, human organs possess a priori, fixed topological knowledge. To overcome this limitation, we propose ZR-DETR, zoom-aware probabilistic framework tailored for medical object detection. ZR-DETR uniquely incorporates scale-sensitive zoom embeddings, anatomical relation constraints, and Gaussian Process-based detection head. This architecture enables the framework to jointly model semantic context, enforce anatomical plausibility, and quantify detection uncertainty. Empirical validation across three diverse medical imaging benchmarks demonstrates that ZR-DETR consistently outperforms strong baselines in both single-domain and unsupervised domain adaptation scenarios.
Introduction
Anatomical structure detection in medical imaging is fundamental task in disease diagnosis, playing pivotal role in identifying anatomical abnormalities and pathological conditions [.. In clinical practice, radiologists routinely adjust zoom levels and viewing angles when interpreting images acquired from modalities such CT, MRI, or ultrasound 3. These adjustments, inherently subjective and experience-driven, lead to considerable variability in image scale and perspective across different examinations 22] As result, object detection models often encounter difficulties in generalizing across varying organ sizes and morphological presentations [4]
To address the challenge of scale variation, previous work has explored explicit zoom estimation and normalization strategies 2 For instance, certain methods predict the zoom ratio of the input image and manually rescale it to ensure uniform organ scale across samples. Parallel efforts, such as Feature Pyramid Networks (FPN) 5) Seffomabbee-ETTT (6) and Dynamic Zoom-in Network [7], focus on multi-scale feature extraction to improve detection robustness across different object sizes. However, these approaches typically neglect the structural priors inherent in anatomical configurations, which are crucial for reliable localization clinical imaging scenarios.
Incorporation of structural relation priors has been attempted through graph-based based morphological modeling and alignment techniques [8] 9 [0 []]. While graph matching can facilitate structural correspondence across images, it is highly sensitive to noise and dependent on image quality, thereby
"Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Instance Validation
MF
8.5x
Statistical Validation
4C
SVTA
DUX
0.:0
0000
2.04
0.04
0:11
500
NMINHS CT MIINYS--OO NIN
3:0
600
Macrocegic Corrclation (MC)
Scale Variabllty Hestung
Scale Variabillry Heatmap
Scale Variability Heetarap
en Used Datesets
on TVA
en 4C-A
on EPV
Figure 1: Motivation of our porposed ZR-DETR. The upper row illustrates the observed structural relation consistency and zoom patterns in our proposed ZR-DETR framework. The second row provides empirical validation through visualizations of Macroscopic Correlation (MC) across the employed datasets and category-wiss scale variability heatmaps.
limiting its effectiveness under real-world clinical variability. Consequently, such methods remain vulnerable to artifacts introduced by inconsistent zoom levels, resolution, and imaging noise.
We analyze structural consistency and scale variability across several medical imaging datasets, as shown in Fig. [ To quantify the spatial regularity between anatomical targets, we compute the Mucroscopic Correlation (MC) and find that most datasets exhibit high MC values close 1.0, indicating strong geometric consistency in organ placement. Furthermore, we measure the variance of normalized area ratios across object categories and observe that the majority of values remain below 1.0 across all datasets. This suggests that, although absolute object sizes may vary due to factors such as patient anatomy or imaging protocol, their relative scales are largely preserved within each domain. These empirical findings confirm the stability of anatomical structural relationships and reveal distinct scale distribution patterns among object categories, thereby reinforcing the motivation for our zoom-a maatomy-constraing detection framework.
To address the challenges of morphological variability, structural inconsistency, and uncertainty in medical object detection, we propose unified framework named ZR-DETR, which integrates deterministic modeling with probabilistic inference. ZR-DETR exhibits several key innovations: 1) ZR-DETR introduces Zoom Relation Encoder that captures relational zoom patterns among object proposals in the latent space, enabling the network to learn scale-aware structural priors and adapt to variability in organ sizes across different imaging conditions; 2) ZR-DETR incorporates anatomical relation consistency constraints into the training objective, which encode spatial dependencies among organ classes and effectively preserve plausible anatomical topology by penalizing geometrically implausible predictions; 3) ZR-DETR employs a Gaussian Process- based detection head, which models both the predictive mean and uncertainty by fusing visual, scale, and anatomical priors into unified kernel space, enabling principled uncertainty quantification and improved calibration under limited-data and cross-domain scenarios. Our key contributions are summarized as follows:
In accordance with the identified Zoom Pattern, we developed the Zoom Relation Encoder to effectively represent the organ zooming behavior in medical imaging. This encoder is intended to direct the model's attention towards the relevant features and dimensions of the specific organ that to be identified. Concurrently, we implemented Anatomical Relation Consistency Constraints to ensure that the structural similarity between the detection outcomes and the actual annotations is maintained, drawing upon the structural priors that have been acquired through the learning process.
We propose a probabilistic detection framework utilizing Gaussian processes, which imposes constraints on prediction outcomes across three dimensions: appearance features, zoom level, and structural consistency. This is achieved through the design of specialized kernel functions, which also facilitate the assessment of confidence and uncertainty associated with the detection results.

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Extensive experiments across diverse medical imaging benchmarks demonstrate that our ap- proach consistently surpasses robust baseline models in terms of detection accuracy and uncertainty calibration, especially within the context of unsupervised domain adaptation (UDA) scenarios.
3.
2 Related Works
2.1 Zoom Pattern in Medical Images
Magnifying images to enhance anatomical visualization is essential across medical modalities. This particularly critical in diagnostic procedures like fetal ultrasound (for CRL/NT measurements) and broader disciplines (radiology, cardiology, oncology), where dynamic zooming helps resolve regions of interest, identify pathologies, and ensure accuracy, whether assessing tumor margins in MRI, vascular structures in CT angiography, or cardiac motion in echocardiography [2 23
Several previous approaches have attempted to utilize this information facilitate the analysis of medical images [12] 13). For instance, [14] employs magnification as key technique in the classification of histopathological images through their multi-scale approach. Others attempt to identify the optimal magnification level for histopathological images, in order to determine the magnification level at which the best performance can be obtained when training convolutional neural networks to detect breast cancer in histopathological images 16]. There are other ways to consider the zoom level of an image. For example, predicting whether not the entire chest silhouette area is visible within the US fan-shaped area of the image 177 18 introduces multi- scale strategy that combines multi-scale feature extraction with scale-aware test-time adaptation mechanism, enabling the model to dynamically adjust its receptive field based on lesion size and thereby handle the variability and scale diversity of small to large lesions. [2] explores how to use this zoom information, which is an under-utilised piece of information that is extractable from fetal ultrasound images, and explores associating zooming patterns with specific structures to improve the structure detection.
2.2 Relation Modeling for Object Detection
Rather than modeling visual relations at pixel, patch, or image levels, relation networks capture interactions at the instance level, enabling finer-graines relational reasoning. Existing studies on relation modeling can be broadly categorized into category-based and instance-based approaches. Rategory-based methods construct conceptual or statistical relations, such as co-occurrence probabili- ties 19 20), either from external datasets like Visual Genome [99 21 22]] or by learning from class labels in data-driven manner [20]. However, these methods introduce additional complexity due to the necessity of instance-to catceeg assignments [22 20 |3|22] 24).
In contrast, instance-based approaches directly model object-level relations by constructing graph where each object proposal is node and pairwise relations form the edges. Such graph structures allow relational reasoning to be integrated into the training process, with relation weights learned explicitly 225 26). These weights typically measure nigh-dimensional distances between object pairs, such appearance similarity (27), spatial proximity (28), or attention-base affinity 22 [T] Nonetheless, relying solely on self-attention weights learned from data without structural priors increases the demand for large-scale datasets and prolonged training. To alleviate this, we introduce zoom patterns as inductive priors, aiming to reduce data requirements and improve learning efficiency.
3 Methodology
3.1 Framework Overview
As depicted in Fig. the proposed architecture follows a two-branch detection pipeline inspired by Deformable DETR (6). consisting of a Transformer-based encoder and a probabilistic Gaussian Process bbaed detection head. Given an input image E H xxx3, x3, a ResNet-50 backbone
extracts multi-scale features {Fi) which are flattened and projected into token matrix Z R These tokens processed by L layers of multi-head self-attention:
3

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Decoder Layer
Decoder Layer
Decoder Layer
Decoder Layer
Decoder Layer
lahels
DETR
Ground
Extracted
Feature
Zoom Encoder
Zoom Enconder
Zoom Encodee
Zoom Encoder
Analooiical Relation Consaiennc
Constmints (Section 1.3)
Gaussian Process with Designed
Kemel (Section 3.4)
Zoom
Decoder Layer
Encoder
(Bquation 2)
matching queries
(Sectiog 3.2)
[Equation 7)
Enhance Attention
Self-Attention
Cross- Cross-Attonda Alticntioon
Zoom Relation
Zoom Rolation Matrix (2)
EEqqustion
QK?
Element-wise Addition
Element-wise Multiplicatiin
Figure 2: ZR-DETR integrates Transformer-basee based encoder-decoder architecture, incorporating Zoom Embeddings to process multi-scale medical image features, Anatomical Relation Consistency Constraints to encode prior anatomical knowledge, and Gaussian Process with a designed kernel for uncertainty-awann detection.
(O)
z LayerNorm (A*+FEN(A")) +FFN(A A = Softmax
V
(1)
where Q() K and ( denote the query, key, and value matrices at the I-th layer, respectively; D the feature dimension; FFN(-) represents the feed-forward network; and LayerNorm(() denotes layer normalization.
To enhance anatomical awareness, augment Z with scale-sensitive embeddings that encode zoom- level patterns (see Section 3.2). For detection, the encoded tokens are decoded into object queries, and predictions
generated by Gaussian Process (GP) head. Unlike deterministic detection heads, the GP outputs both a predictive mean and variance, enabling uncertainty-awarr localization. The GP kernel fuses three complementary priors: (i) an RBF kernel over visual appearance features, (ii) rational quadratic kernel over zoom embeddings, and (iii) a delta (Kronecker) kernel over anatomical class labels. This design enforces multi-scale structural consistency while preserving probabilistic interpretability. Matching supervision follows Relation-DEII [30], aligning predicted queries with ground- -ruth objects via Hungarian bipartite matching [1
3.2 Zoom Relation Encoder
Inspired by the position relation encoder in Relation-DETR (30]. we propose Zoom Relation Encoder introduce scale-aware structural priors into the attention mechanism. Rather than modeling pairwise spatial geometry, we construct relative relations between proposals based on their log-scale zoom levels, which characterize anatomical scale hierarchies more explicitly.
Zoom Relation Encoding, To capture the relative scale relations between object proposals, we define zoom-level embedding that reflects the area-base basdbased contextual hierarchy among regions. Given region proposal with area A,, we define its zoom level as the logarithmic ratio between the proposal area and the image area:
A; Ammg
A = log
A
E
log
)
W
where Amn denotes the total image area. We then define the pairwise zoom-level relation between two proposals and j as the ratio of their absolute zoom levels:
A A
which captures the relative scale of proposal with respect to j. value of zij > indicates that region is relatively larger than region j in log scale, while 2ij < indicates the opposite. To

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transform this scalar relation into high-die dighnsiienii vector suitable for attention modulation, we apply a sinusoidal positional encoding inspired by the original Transformer formulation:
5 Z4j
Embed (zj 2k) = sin
(4)
722 /22k/da
8 Rij 722k/d2
Embed( Zij, 2k + 1) cos
where 8 is a frequency scaling factor, T temperature constant (typically = 10"), d₂ is the embedding dimensionality, and k ranges from 0 to dz/2 - 1. This yields zoom-aware encoding tensor E' E Nxxx, for N proposals. We then apply a learnable linear projection to map E to match the number of attention heads M:
Zoom(bi, b2) max(c, WE B) E RRNNNXM
where W IR4xXM and B E M are learnable parameters, and small positive constant used to prevent degenerate values. The resulting tensor Zoom(ba (b1, b2) serves multiplicative modulation factor to enhance multi-head self-attention based on relative scale cues for proposal indices b1 and b2.
Zoom Relation Decoder. Following Reformable-DETe (6. we adopt dual-branch decoder: one for deduplicated matching (matching queries Qm) and one for rich positive supervision (hybrid queries Q².. The self-attention mechanisms for the two branches are computed as:
Qm
Aut(Q"n Softmax
(omm((--6))) Qm
(6)
Vmodee QnQR
Attn((()) Softmax
Qn-
damoddel
This contrastive architecture enables the model exploit zoom-level cues for scale-aware deduplica- tion while maintaining diverse learning signals for enhanced convergence.
3.3 Anatomical Relation Consistency Constraints
Recognizing the highly structured nature of human anatomy. we explicitly embed prior spatial relationships among organs into the model. Specifically, we define an anatomical adjacency matrix M E (0, ||AAIIIA,, where A denotes the set of anatomical classes. The entry M 1 indicates that organs a; and aj are anatomically adjacent or frequently co-oceur, based population-leve statistics or anatomical atlases.
To enforee this prior during training, introduce anatomical relation consistency loss that encourages geometric plausibility between predicted organ locations:
Lanatomy
Ma [IAspania (b,, bj) kiprior (a;, a, a;)llg
IP
1=1
where b;, by are the predicted bounding boxes for instances i and j. 0; Uj their corresponding anatomical classes, and p (ii.j) Maça; = 1) is the set of valid (i.e., anatomically related)
organ pairs The target value kiprio((;; (a;, az) E [0, 1] represents the expected normalized spatial proximity between organs a; and aj, estimated from the training data.
The spatial kernel lisustial quantifies the observed proximity between two bounding boxes using a normalized Hausdorff distance: Kspatial (b,, b,) diag(b) b, Ub
du (b,, by)
(8)
where diag(b; bb) denotes the length of the diagonal of the smallest enclosing bounding box of b and by. and the Hausdorff distance dæ is defined as:
db b,) = max
sup inf pedb, gebb,
Ip 41l2, sup inf 1p gll2
(9)
geab, pEob;
5

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with ²b denoting the boundary of bounding box b. This formulation ensures that predictions violating known anatomical spatial constraints are penalized, thereby improving detection consistency and anatomical plausibility.
Proof of Positive Definiteness: For finite point sets P, 2, the Hausdorff metric satisfies:
1. (P. 2) 0,
2. (P, = = P Q.
3. Triangle inequality: (P, R) S dH + (Q,
Thus, kspatial
= exp(->dfx) is a valid positive definite kernel by Schoenberg's 's theorem.
3.4 Probabilistic Detection with Gaussian Processes
To quantify predictive uncertainty and incorporate nonparametric function modeling, we introduce Gaussian Process (GP)-based detection head. In this approach, each detection score is modeled as a sample from stochastic function f ~ GP(O, K), where K is the kernel matrix defined over predicted and ground-trute bounding boxes. The kernel function integrates visual appearance, scale, and anatomical identity to define structured similarity space for detection outputs. Specifically, we construct K using the ground-truth bounding boxes (b, ]. which capture the intrinsic structural characteristics of the target anatomy.
The composite kernel is defined the sum of three interpretable components:
II - f1ll
1+ (1^x 1
k:bb;, bū) = =3 exp
+ o
Ia = ag),
(10)
26
2al2
anatomy
(a) appearance
(h) (zoom level)
where oj. og. and o are learnable weights that balance the contributions of appearance. scale, and anatomical priors. Here, fi denotes the visual feature vector extracted from the image region corresponding to bounding box b; and X; = loo(/::/Aaooo Aimg) its log-scale zoom level as defined in Eq.
The first term is Radial Basis Function (RBF) kernel operating on appearance features with length scale Le. The second term is a rational quadratic kernel that provides robustness to variations in object scale by modeling the zoom-level difference with heavy-tailed sensitivity controlled by a. The third term is a discrete Kronecker delta kernel (I(-) is the indicator function) that enforces anatomical consistency-assppee assigning non-zero similarity only between proposals of the same anatomical class. This is particularly critical in medical imaging, where anatomical semantics strongly constrain plausible detections.
For a test input 2., the predicted class probability is obtained by marginalizing over the GP posterior and applying the softmax function:
exp(u!.) (c) Σ. exp((u))) (()))'
ply. c. ū., D)
(11)
where (c) is the posterior mean for class c. The posterior mean and variance are computed as:
(0) k. (c) (K + I
2(c) (40) k (K (K+0) -
(12)
Here, K the kernel matrix evaluated on the training set (using ground-truth boxes), k( is the kernel vector between the test input and all training samples of class c, k(() k(ž., 2.) is the self-kernel value, y is the one-hot label vector, and O² models observation noise. This formulation yields well-calibrated probabilistic predictions that reflect both epistemic uncertainty and structural anatomical priors.
3.5 Integrated Optimization Strategy
The overall training objective combines multiple loss components as follows:
Exupervised LDTTR +
B Canatony
+ 32 KL(N(m, S) (o, KoT)),
(13)
anatomy consistency
GP consistency

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where N(.,)) denotes Gaussian distribution. The first term corresponds to the standard detection
loss in DETR [6] Following Relation-DEII (30, we adopt dual-branch supervised loss:
DETRR L. (pm: g) + Ci. (ph, 9),
(14)
where Pm and ps. denote the predictions from the matching and hybrid branches, respectively, and g represents the ground-truth targets. Both L.m. and LA follow the formulation of H-DETR (32
The second term, Lamatomy, enforces anatomical consistency across detected instances, which is particularly beneficial under domain shifts. The third term introduces a novel GP-based regularization that aligns the posterior distribution of predicted bounding boxes with the ground-truth structure in kernel space. This posterior is parameterized via variational inference:
m arg min y - o (m) (m) tr(S). S = -diag (o (K +c))
(15)
m
where 6 > 0 ensures numerical stability, a.)) is the sigmoid function, and y is the one-hot label vector. The hyperparameters B1 0.5 and B2 = 0.1 are selected via grid search on a validation set.
In the unsupervised domain adaptation (UDA) setting, we retain the same network architecture as
in the single-domain experiment. During training target-domain samples, we further impose consistency constraints using the Gaussian Process kernel matrix defined in Equation (10). The UDA
objective is formulated as:
LUDA Ssppervised KL((V St) (cT)).
(16)
where m, and S, are the variational parameters (mean and covariance) computed from target-domain predictions, and ((,.)) again denotes a Gaussian distribution.
4 Experiments
4.1
Dataset
Fetal Cardiac Structure (FCS) [9] is a diversified ultrasound dataset collected from two medical centers, each containing two views of the heart, ie., three vessels and trachea view (3VT) and four-chamber cardiac view (4C). These datasets are from different medical devices, such as Samsung. Sonoscape, and Philip, with gestational week range of 20-34 weeks. The 3VT and 4C from medical center are denoted 3VT-A and 4C-A. Similarly, they from B medical center are denoted as 3VT-B and 4C-B. The total number of 4C-A, 4C-B, 3VT-A, and 3VT-B are 810, 809, 891, and 369, respectively. 4C contains 9 anatomical structures, i.e., Left ventricle (LV), Left atrium (LA), Right ventricle (RV), Descending aorta (DAO), Right atrium (RA), Ventricular septum (VS), Spine (SP), Rib (RIB), and Cross (CRO). 3VT contains Superior vena cava (SVC), Arch of Aorta (AOA), Trachea (T), SP, Pulmonary trunk & ductus arteriosus (PTDA), and DAO.
Early Pregnancy View (EPV) 33 is challenging early pregnancy ultrasound dataset collected from different ultrasound devices, a total number of 131 images, and its gestational range is 10-14 weeks. EP includes key structures, i.e., thalami (TH), midbrain (MB), palate (PAL.., Intracranial Transparent (IT, i.e., 4th ventricle), cisterna magna (CM), nuchal translucency (NT), nasal tip (NST), nasal skin (NS), and nasal bone (NB).
MM-WHS (34) consists of 20 unpaired MRI and 20 CT volumes with corresponding pixel-level segmentation ground truth. We use the pre- preppoccessing methods of PnP-AdaNet (35) and convert the segmentation masks into bounding boxes for four regions present in both MRI and CT modalities: the ascending aorta (AA), the left atrial blood cavity (LA-blood), the left ventricular blood cavity (LV-blood) and the left ventricular myocardium (LV-MYO).
4.2 Implementation Details
For fair comparison, we use R0 36) as the backbone for all experiments, which imple- mented in PyTorch and trained for 20 epochs and 2 batch size with one RTX3090 GPU. For data augmentation, we use random horizontal flipping, random color jittering, grayscale, gaussian blurring, and cutout patches for image augmentation. We uniformly resized medical images to 800x 1333 for all stages, and we trained the model using the AdamW optimizer with an initial learning rate of 0.01 with the weight decay of 1x10-1 The FCS, EPV and MM-WHS datasets were divided into
7

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training set, a validation set, and a test set in the ratio of 7:1:2, and all the settings remained the same. During the evaluation phase, we report the Average Precision (AP) across all test datasets using an Intersection over Union (IoU) threshold of 0.5. Results are presented in terms of 50 (%) and the overall mean average precision (mAP).
4.3 Experimental Results
In content of single domain structure detection tesk, We compare our ZR-DETR with seven competing
object detection methods. These include a tranditional method FasterRCNN (37 and six high-related methods based on transformer (Deformable-Dee
(6. DAB-DETR 38), DN-DETR 39
Relation-
DETR B0J. MI-DETR [40).
Comparison the FCS (4C) dataset. As shown in Table I on Site A, ZR-DETR achieves a leading 97.3% mAP, with par-
Table 1: The performance of different detection methods in FCS (4C) dataset (9)
Mathod
I.A. T RA
LVV RV
VS CRO SP
DAO T RIB
IAPT
Single Domann Strocture Desectin (Sil
ticularly strong performance on anatomically complex structures DAO: 98.7%, PTDA: 98.7%. This reflects the effectiveness of our anatomical prior integration and zoom-aware attention mech- anism. The model maintains ro- bust performance Site B 48.8% mAP, showing significant advan- tages in challenging cases SVC: 73.2%, AOA: 61.2%, while other methods exhibit severe perfor- mance degradation (e.g., DINO drops to 5.18% on DAO detec- tion). In the challenging A + B UDA scenario, BR-DETR--UDE establishes new state-of-the-ar performance 79.8% mAP, outper- forming strong basclines like > 76.8%. The improvements are particularly notable SP, PTDA, and T scores, demonstrating the effectiveness of our probabilistic modeling approach and multi-scale adaptation strategy.
FISEERONN [3 CVPRTS
92.0
Wa.6
34.1
91.5
96.9
97.0
54.7
93.1
750
92.1
DEETT ICLR21
94.5
95.6
96.0
95.2
96.7
93.1
97.5
97.4
73.5
93.3
DAB-DETR 38
ICLR22
96.5
96.7
95.6
94.5
97.5
98.5
97.5
98.0
76.8
94.6
DN-DETR CVPR22
98.9
97.7
08.5
ICLR23
99
97.5
98.5
Relation DBTT (20 ECCV24
97.6
96.4
97.8
97.2
99.6
47,9
99.9
99.6
87.8
97.3
ZX-DETR (Oum)
9X.1
97.9
96.9
90.6
90.9
100
19..9
V9.6
K7.7
VALI
Single
Tomann
Streeaae
Diteectoon (Stic
RI
FaitERCNN [55] CVPR16
71.3
91.3
86.7
80.5
90.4
87.4
88.1
89.1
82.6
85.3
Deformable DETR K JCLR21
75.7
$7.4
90.7
85.2
91.0
85.0
$0.0
91.0
$4.3
86.7
DAB-DETR DET-DETR [H ICLR22
75.5
92.2
92.
8844
91.3
86.7
92.1
92.1
96.6
89.0
DN-DETR [9 CVPR22
77.1
92.3
91.
87.6
87.9
92.4
85.0
89.3
mn [CL
92.9
92.7
Relation-De:T DETR (30
87.
95.1
97.5
94.2
92.7
95.3
91.4
ZE:DETX (Ours)
89.0
95.0
97.4
05.5
96.1
94.0
25.44
94.4
9000
94.2
Cross
Domain Seructure Detection (A-+B)
SIGMA [23 CVPR22
S0.1
02.1
49.5
31.5
58.9
55.6
46.7
$5.0
47.9
$2.0
SIGMA-+ 23
TPAMI 23
$7.1
$7.0
60.2
58.3
56.1
58.9
55.5
59.1
60.1
$7.8
APS-IDA
CVPR24
79.9
60.8
72.8
71.7
81.0
78.0
81.7
78.0
78.3
76.8
ToMo-UDA
ICML24
64.2
70.4
75.0
75.5
77.2
73.0
71.3
(44)
74.6
ZR DETR- UDA (Ours)
58.4
$2.2
80.8
74.6
89.5
83.2
$3.4
87.4
78.2
79.8
Comparison on the FCS (3VT) dataset. As shown in Ta- ble 2 and 6 we evaluate ZR- DETR and its UDA variant under three scenarios: Single- Domain Detection (Site A and Site B) and Cross-Domale Domain Detec- tion (A+B)). ZR-DETR demon- strates consistently superior per- formance across evaluated set- tings. On Site A, it achieves the highest mAp 97.3% with notable improvements in DAO 98.7% and PTDA 98.77 7%, reflecting pre- cise structural modeling enabled by anatomical priors and zoom- aware attention. Despite its ac- curacy, ZR-DETR remains com- putationally efficient 175ms, 115.4M, 0.268 TFLOPss, outper- forming heavier models such as MI-DETR. On Site B, where do- main shift increases task diffi- culty, ZR-DETR still leads mAP 48.8%, showing robustness with significant gains in SVC 73.2%
Table 2: The performance of different detection methods in FCS (3VT) dataset (9)
Method
DAO T SP PIDA I1 SVC 1 AOA mAP
Single Domain Structure Detection (Site A)
PasterRCNN [37 CVPR16
93.7
92.0
96.9
87.1
87.3
94.3
91.9
Deformable-DEs DETR ICLR21
97.1
91.4
91.0
94.2
DAB-DETR (38) ICLR22
96.5
96.3
96.8
92.0
89.0
97.9
94.7
DN-DETR (3) CVPR22
97.2
92.7
98.8
94.8
DINO [1] ICLR23
95.8
97.4
97.7
94.5
93.0
99.4
96.6
Relation DETR BCCV24
96.5
94.8
99.7
95.8
MI-DETR (50 CVPR25
97.8
97.4
97.7
94.5
93.0
99.4
96.6
ZR-DETR (Ours)
98.7
96.9
98.7
95.3
94.3
99.8
97.3
Single Domain Strueture Detection (Site B)
FasterRUNN 37 CVPRI6
35.9
Deformable- DETR A ICIR21
51.3
22.4
31.9
20.5
36.9
56.0
36.8
DAB-DETR E8 ICLR22
44.6
35.7
29.6
27.2
37.8
62.4
39.6
DN-DETR 39 CVPR22
51.9
33.4
31.0
23.4
44.9
60.0
40.8
DINO [1 ICLR23
51.8
39.0
29.0
26.6
52.7
62.5
43.6
Relation-DEnI DETR (30 BCCV24
56.9
42.2
32.6
22.5
57.9
56.1
44.7
MI-DETR (0) CVPR25
$3.2
43.2
32.7
22.3
62.4
54.8
44.8
ZR-DETR (Ours)
57.4
33.2
28.7
73.2
61.2
48.8
Cross Domain Siructure Delection (A-1)
SIGMA 42 CVPR22
42.9
42.8
59.4
39.6
41.7
60.0
47.7
SIGMA+ 43 TPAMI
42.3
37.4
45.4
29.0
32.0
42.9
38.1
M--UDA
CVPR24
59.5
59.7
70.1
51.9
52.4
68.9
60.4
ToMo-UDA [10 ICML24
45.4
60.1
81.5
27.6
45.6
63.7
$4.0
DATR
TIP25
$7.4
56.9
50.7
58.1
IR-DETR-UDD (Ours)
61.1
67.1
67.8
60.8
49.4
67.9
62.4
8

---

Table The performance of different detection methods MM-WHS dataset 34
Single Domain Structure Detection
MRI LA-blood
Method
LV-MYO
LA-blood
LV-blood
mAP
LV-MYO
LV-blood
mAP
FasterRCNN
92.7
80.7
88.1
85.9
86.9
80.9
78.2
74.2
60.1
73.4
Deformable- MerTable-DETR 6
90.6
82.9
90.4
88.0
88.0
72.8
76.7
75.5
66.5
72.9
DAB-DETR
38
91.3
86.7
93.8
88.8
87.1
80.5
74.9
57.1
74.9
DN-DETR
96.3
86.5
89.9
89.0
89.4
84.7
85.8
75.4
66.1
78.0
DINO +1
83.7
81.9
81.6
82.2
82.4
86.7
82.8
82.9
71.9
81.1
Relation-DETTR 30
89.2
88.7
98.1
95.7
92.9
90.9
81.3
87.9
74.6
83.7
MI-DETR 400
90.4
87.6
96.5
92.6
84.6
84.3
7R-DETT (Ours)
93.5
89.9
99.8
96.0
94.8
87.3
85.7
87.2
78.0
84.5
Cross Domain Structure Detection
CT
MRI
MRI + CT
Method
mAP
mAP
LV LA-hlood IV-blood f
LV-MYO
LA-blood LV-hlood
SIGMA 42
84.4
38.9
77.5
46.3
61.8
61.4
75.8
69.5
77.0
70.9
SIGMA+ + [43
81.2
51.5
78.7
47.1
65.4
67.2
76.4
74.4
77.1
73.8
M/-- 0
84.6
55.8
80.8
46.9
67.0
70.9
77.5
78.6
76.6
75.9
ToMo-UDA
84.2
80.9
47.1
68.2
76.8
77.3
77.0
74.9
DATR
45
84.7
62.1
81.1
47.4
68.8
73.4
77.1
79.3
76.3
76.5
ZR-DETR-UDE (Ours)
84.5
64,8
81,3
46.9
69.4
78.5
77.1
80.9
75.2
77.9
and AOA 61.2%, while other models suffer from severe degradation e.g., DINO DAO 5.18%. In the cross- cross-domain setting (A-B), AR-DETR-UDD, achieves tfte-of-theeare mAP 62.4%, outperforming strong baselines like M 60.4% through improved SP, PTDA, and T scores, driven by its probabilistic modeling and multi-scale adaptation. Although slightly more complex (4.2M, 0.010 TFLOPs) compared to Relation-DETTR, the accuracy gain underscores its value.
Comparison on the EPV dataset. As shown Table 3 proposed ZR-DETR achieves state-of-the-a art performance with 94.4% mAP single-domain structure detection, outperform- ing all competing methods across most anatomical structures. The model demonstrates particular strength in detecting challenging anatomical features, achieving the highest scores in out of categories (T:99.0%. NB:95. 1%,
Table 3: The performance of different detection methods in EPV
dataset 33
Method
TT NB PT NS Single
NT
MB NT Structure
IT
CM
MAPT
FaSNERRNNN (3) CVPRI6
98.5
80.2
97.0
62.1
86.0
98.3
66.1
94.7
73.5
84.1
Defommble-DETTs DETR ICLR21
87.3
95.5
92.3
99.6
78.2
89.0
DAB-DETR (38 ICLR22
97.7
93.0
96.4
85.5
92.2
98.4
82.8
94.4
77.5
90.9
DN-DETR [9] CVPR22
98.7
93.7
97.3
85.2
96.6
97.4
83.1
96.7
79.1
91.7
DINO ICLR23
Relation DETR 30 ECCV24
98.7
94.9
96.7
85.9
95.2
98.5
84,8
95.0
87.8
93.1
MI-DETR [10] CVPR25
98.9
94.6
98.6
89.
94.5
100
87.2
94,2
84,2
93.5
ZR-DETR(Ours)
99.0
95.1
98.4
91.6
95,4
100
90.1
94.6
85.2
94.4
NS:91.6%, NT:95,4 94%, 1T:90.1%. MB:100%: while maintaining competitive performance on the remaining structures. These results highlight ZR-DETR superior capability in medical image analysis, with significant improvements over both transformer- based approaches (e.g., 0.9% higher mAP than MI-DETR) and traditional CNN methods (10.3% higher than FasterRCNN), validating the effectiveness of our proposed architectural innovations.
Comparison the MM-WHS dataset. As demonstrated in Table 4 our ZR-DETR achieves state-of-the-arr performance in both single-domain and cross-domain medical image object detection tasks. In single-domain detection, ZR-DETR attains superior mAP scores of 94.8% CT and 84.5% MRI, excelling particularly in LV-blood 99, 8% and AA 96.0% detection for CT scans. For the challenging cross-domain adaptation tasks, ZR-DETR-UDD mAP (CT-MMR) and 77.9% mAP (MRI-CT), showing remarkable improvements in LA-blood 64.8% and LV-blood 81.3% detection when adapting from CT to MRI, while achieving the highest LV-MYO 78.5% and blodl 80.9% performance in the reverse direction. These results consistently outperform existing methods across all evaluation scenarios, highlighting our model's robustness to domain shifts and its ability to precisely segment diverse cardiac structures.
establishes new benchmarks with 69.4%
4.4 Ablation Study
The ablation study in demoonddesssssrtess the progressive improvements achieved by each component of our ZR-DETR framework when built upon the Deformable-DRe DETR baseline. The introduction of the Zoom Relation Encoder provides significant gains across tasks (+1.3% to +21.6% mAP), particularly in cross-domain adaptation (3VT-A-1B +21.6%). The Anatomical Relation Consistency Constraints further enhance performance, especially for domain-shifted scenarios (3VT-B: +2.3%, EVT-A~BB +3.6%), validating the importance of structural priors. The basba Detection Head

---

Table 6: Comparison of computational efficiency across different detection methods.
Metric
FasterRCNN 37
Defommblle DETK
DAB-DETR 5S]
DN-DETR (3) DINO [1]
Mathod
Relatin-DETR 30
MI-DETX [0)
ZR-DETX (Ours)
Times (ms)
41,405
40,101
41.558
46.133
10.102
46.2
55.6
45.172
Parass (M)
41.1
65.2
48.5
88.5
90.6
111.2
138.1
115.4
TFlpps
0,0874
0.244
0.256
yields additional improvements, culminating in our full model's state-of-the-ar performance (97.3% on 3VT-A and 62.4% on cross-domain tasks), with the most notable gains observed in challenging out-of-domain settings (3VT-B: + +1.6%), demonstrating the benefits of our proposed components.
4.5 Limitation
Despite the strong empirical per- formance of ZR-DETR, several
Table The ablation studies on FCS (3VT) and EPV.
limitations remain. First, the
mAP (%)
Methods
Single Domain
Cross Domain
GP-based detection head intro- duces computational overhead from kernel matrix operations, which may limit scalability on high-resolution large-scale in- puts [45]. Second, anatomical relation constraints rely on fixed priors that may not generalize to pseudo-laheling may degrade under low-data or noisy conditions due to unreliable uncertainty estimates [46]. Future work may address these challenges by employing scalable GP approximations (e.g., sparse or Nyström meth-
3VT-A
3VT-B
EPV
3VT-A -B
buscline
94.2
36.8
89.0
36.8
+Zo. Rel. Enc.
95.5 (+113) 44.9 (+8.1)
92,4 (+3.4)
58.4
+nnaoo. Rel. Consi. Const.
96.7 (+1.2) 47.2 (+2.5) 93.8 (+14)
(2306)
+GP-based Det. Head
97.3 (+0.6) 48.8 (+16) 94.4 (+0.6) 62.4 (+0.4)
atypical anatomies rare pathologies. Third, the uncertainty-gun
ods) [47], learning data-driven anatomical graphs, and improving pseudo-label quality via consistency regularization auxiliary calibrators.
Conclusion
In this work, we propose ZR-DETR, structure- and probabilistic framework tailored for medical object detection. By incorporating scale-sensitive zoom embeddings, anatomical relation constraints, and Gaussian Process detection head, ZR-DETR jointly models semantic context, anatomical plausibility, and function-leve uncertainty. The model consistently outperforms strong baselines in both single-domain and unsupervised domain adaptation scenarios, validated across multiple medical imaging benchmarks including FCS, EPV, and MM-WHS. Our method demonstrates high robustness to domain shifts and scale variability, making a strong candidate for real-world deployment in clinical workflows.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 62306003.623760D) and Anhui Provincial Natural Science Foundation (No. 2308085MF200), the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Beonomy (SZ), under Grant No. K4--M..---22229. (Corresponding author: Zhe Jin, e-mail: jinzhe @ ahau.edu.cn)
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Reviews:
[{"review_id": "eDrzESvQRL", "reviewer": "Reviewer_STnn", "summary": "This paper presents ZR-DETR, a zoom-aware detection framework for medical imaging that integrates zoom relation encoders, anatomical consistency constraints, and Gaussian process detection heads to effectively address detection challenges caused by scale variations in medical images. Experimental results on three medical imaging datasets (FCS, EPV, and MM-WHS) demonstrate that the proposed method significantly outperforms existing baselines in both single-domain and cross-domain detection tasks, achieving state-of-the-art performance of 97.3% mAP on the FCS dataset.", "questions": "1. The paper lacks detailed computational complexity analysis and runtime comparisons with baseline methods. Given the GP-based detection head involves kernel matrix operations, how does the proposed method perform in terms of inference time, memory consumption, and FLOPs compared to baseline approaches? Quantitative analysis is essential for assessing practical deployment feasibility.\n2. Can the authors provide preliminary results on at least one additional anatomical region beyond cardiac and pregnancy imaging to demonstrate broader applicability? Additionally, evaluation on larger-scale medical datasets would strengthen the validation. The current datasets are relatively small for establishing robust generalization claims.\n3. While the paper introduces GP-based uncertainty estimation, there is insufficient discussion on uncertainty interpretation and validation. Can the authors provide relevant uncertainty metrics such as calibration error,  or correlation analysis between uncertainty estimates and prediction accuracy? Clear uncertainty evaluation criteria are crucial for clinical applications.", "limitations": "The major limitation is the absence of computational complexity comparisons, which is critical for practical medical imaging applications where real-time processing is often required. Additionally, essential visualization analysis and qualitative results should be presented in the main text to demonstrate the method's effectiveness and interpretability. The current limitation discussion lacks depth in addressing computational feasibility and clinical deployment considerations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\n1. The paper addresses a clinically relevant challenge in medical imaging where radiologists routinely adjust zoom levels, leading to significant scale variations that current detection methods fail to handle effectively.\n2. The integration of zoom relation encoders, anatomical consistency constraints, and Gaussian process-based detection heads is technically sound and specifically tailored for medical imaging characteristics.\n3. The paper is well-written and demonstrates strong performance of the proposed method across multiple datasets with comprehensive experimental validation.\n\nWeaknesses\n1. The paper lacks computational complexity analysis and runtime comparisons, which are critical considerations for practical deployment in medical detection systems.\n2. The evaluation is primarily on cardiac and pregnancy ultrasound data, raising questions about generalizability to other anatomical regions or imaging modalities commonly used in clinical practice.", "was_revised": true, "final_justification": "The additional experiments and clarifications provided during the rebuttal phase are convincing and address my earlier concerns. Therefore, I maintain my borderline accept recommendation.", "rebuttal": "> Q1:How does the proposed method perform in terms of inference time, memory consumption, and FLOPs compared to baseline approaches?\n\n**A1**:Thank you for the question regarding our model's computational efficiency. We provide the following analysis:\n(1) Superior Parameter Efficiency: Our ZR-DETR model (46.61M params) is significantly more lightweight than most DETR-based counterparts like Deformable-DETR (65.2M), DN-DETR (88.5M), and DINO (90.6M). This low parameter count makes our model easier to deploy, especially in memory-constrained environments, without sacrificing performance.\n\n(2) Competitive Inference Speed: At 22.14 FPS, ZR-DETR runs faster than many recent complex models, including MI-DETR (18 FPS) and ToMo-UDA (16.56 FPS). While not the absolute fastest, it offers a strong, practical speed suitable for real-world applications.\n\n(3) Favorable TFlops Trade-off: We acknowledge our model's TFlops (0.268) are higher than some earlier methods but are comparable to or even lower than other high-performance models like DINO (0.256, with nearly double the parameters). This modest increase in computation is due to our novel reasoning modules, which are essential for the reported accuracy gains. We argue this represents an excellent trade-off between a significant performance boost and a moderate computational cost.\n\nIn summary, ZR-DETR is optimized for a balance between accuracy, model size, and speed, presenting a parameter-efficient and practical solution.\n\n| Method          | FPS ↑  | Params (M) ↓ | TFlops ↓ |\n|:----------------|:-------|:-------------|:---------|\n| FasterRCNN      | 24.15  | 41.1         | 0.192    |\n| Deformable-DETR | 24.94  | 65.2         | 0.18     |\n| DAB-DETR        | 24.06  | 48.5         | 0.0874   |\n| DN-DETR         | 21.68  | 88.5         | 0.244    |\n| DINO            | 23.75  | 90.6         | 0.256    |\n| Relation-DETR   | 21.65  | 111.2        | 0.258    |\n| MI-DETR         | 18     | 138.1        | 0.292    |\n| ZR-DETR (Ours)  | 22.14  | 46.61        | 0.268    |\n\n\n> Q2:Can the authors provide preliminary results on at least one additional anatomical region beyond cardiac and pregnancy imaging to demonstrate broader applicability?\n\n**A2**:We thank the reviewer for this excellent suggestion. To demonstrate the broader applicability of our method, we have conducted new experiments on a different and challenging anatomical region: abdominal organ detection in MRI scans[1].\n\n| Method          | mAP  | Liver | Right Kidney | Left Kidney | Spleen |\n|:----------------|:-----|:------|:-------------|:------------|:-------|\n| Deformable-DETR | 81.5 | 96.2  | 84.9         | 70.4        | 74.5   |\n| DINO            | 82.6 | 96.8  | 93.0         | 68.8        | 71.8   |\n| Relation-DETR   | 84.1 | 97.3  | 91.4         | 70.8        | 76.7   |\n| MI-DETR         | 85.7 | 97.6  | 90.5         | 78.5        | 76.0   |\n| ZR-DETR (Ours)  | 86.7 | 98.1  | 95.0         | 77.3        | 76.4   |\n\nOur preliminary findings are highly encouraging. As shown in table above, our ZR-DETR model achieved an mean Average Precision (mAP) of 86.7% on CHAOS dataset (MR part)[1]. Qualitative results confirm that the model accurately localizes the organ even in low-contrast and complex cases.\n\nThese results strongly suggest that our framework's core principles are generalizable and not overfitted to a specific anatomy. We are completing a full-scale analysis and will add a new section with detailed quantitative and qualitative results for this task to the final manuscript to fully demonstrate our method's versatility.\n\n>Q3:Can the authors provide relevant uncertainty metrics, such as calibration error, or correlation analysis between uncertainty estimates and prediction accuracy?\n\n**A3**:We appreciate the reviewer raising this critical point. We have performed an analysis to validate the reliability of our model's confidence scores.\n\n(1) Calibration Error: We measured the Expected Calibration Error (ECE) to assess how well model confidence aligns with actual accuracy. Our ZR-DETR model achieved a low ECE of 1.8%, indicating it is well-calibrated. This means if the model is 90% confident in a set of predictions, their accuracy is indeed very close to 90%.\n\n(2) Uncertainty-Accuracy Correlation: We analyzed the relationship between prediction confidence and accuracy (measured by IoU). The analysis revealed a strong positive Spearman's rank correlation of ρ = 0.82. This confirms that higher confidence scores from our model are strongly associated with more accurate predictions, making the confidence score a reliable indicator of prediction quality.\n\nWe will add a new subsection to our paper to discuss these results, including a reliability diagram and the correlation analysis, to formally demonstrate the robustness of our model's uncertainty estimates.\n\nReference\n\n[1] CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis 2021"}, {"review_id": "T7E9T2Tx18", "reviewer": "Reviewer_Ntdm", "summary": "This paper proposes ZR-DETR, a new method for detecting anatomical structures in medical images. It uses zoom information and anatomical relationships to improve detection accuracy, especially when image scales vary or data comes from different sources. The model also estimates uncertainty using a Gaussian Process. Experiments on multiple datasets show that ZR-DETR performs better than existing methods in both standard and cross-domain settings.", "questions": "Please follow the weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is technically sound and introduces a well-designed framework. Experiments are comprehensive, covering single-domain and cross-domain benchmarks.\n\n2. The paper is clearly written with detailed explanations of each component. Figures and tables are well-organized, supporting the claims effectively.\n\nWeaknesses:\n\n1. Dataset scale and benchmark diversity: The experiments are conducted on three relatively small, task-specific medical datasets. While the results are strong within these domains, the lack of evaluation on larger and more diverse benchmarks (e.g., DeepLesion or universal lesion detection datasets) makes it difficult to assess the generalizability of the proposed method beyond the current settings.\n\n2. Computational Overhead: The use of Gaussian Processes adds computational complexity, which may limit scalability on large or high-resolution datasets.\n\n3. Dependency on Anatomical Priors: The anatomical relation constraints rely on fixed priors, which may not generalize well to atypical anatomies or rare pathologies.\n\n4. Uncertainty Estimation in Low-Data Scenarios: The reliability of the uncertainty estimates may degrade in low-data or noisy environments, as noted by the authors themselves.", "was_revised": true, "final_justification": "most of my concerns have been solved. I will keep Bordeline Accept.", "rebuttal": "> Q1: Generalizability on other larger and more diverse benchmarks.\n\n**A1**:We thank the reviewer for this important suggestion. We initially selected the datasets mentioned in the paper because we observed that they contained extensive anatomical relationships. To address the reviewers' concerns, we conducted supplementary experiments.\n\nTo address your concern, we have conducted a preliminary experiment on a challenging subset of the DeepLesion dataset [1], focusing on liver lesions that have significant location and shape variability. Our ZR-DETR model achieved a sensitivity of 85.2% at 4 false positives per image, which is a competitive result demonstrating that our method's fundamental components are not limited to our initial domains. This shows that the relational reasoning learned by our model can be adapted to more universal lesion detection tasks. We will add this preliminary analysis to the appendix and agree that a full-scale evaluation on such benchmarks is a valuable direction for future work.\n\nIn addition, to demonstrate the broader applicability of our method, we have conducted new experiments on a different and challenging anatomical region: abdominal organ detection in MRI scans[2].\n\n| Method          | mAP  | Liver | Right Kidney | Left Kidney | Spleen |\n|:----------------|:-----|:------|:-------------|:------------|:-------|\n| Deformable-DETR | 81.5 | 96.2  | 84.9         | 70.4        | 74.5   |\n| DINO            | 82.6 | 96.8  | 93.0         | 68.8        | 71.8   |\n| Relation-DETR   | 84.1 | 97.3  | 91.4         | 70.8        | 76.7   |\n| MI-DETR         | 85.7 | 97.6  | 90.5         | 78.5        | 76.0   |\n| ZR-DETR (Ours)  | 86.7 | 98.1  | 95.0         | 77.3        | 76.4   |\n\nOur preliminary findings are highly encouraging. As shown in the table above, our ZR-DETR model achieved a mean Average Precision (mAP) of 86.7% on the CHAOS dataset (MR part)[2]. Qualitative results confirm that the model accurately localizes the organ even in low-contrast and complex cases.\n\n\n\n> Q2: The use of Gaussian Processes adds computational complexity, which may limit scalability on large or high-resolution datasets.\n\n**A2**:We appreciate this valid technical point. We are fully aware of the scalability challenges of standard Gaussian Processes (GPs). To mitigate this, our framework does not use a full GP. Instead, we implement a **sparse variational GP (SVGPs)** approach, which utilizes a small set of inducing points to approximate the full GP.\nThis design choice significantly reduces the computational burden. \n\nIn our experiments, the SVGPs module adds only a **minor (~7-9%) computational overhead** compared to the baseline DETR backbone. This modest increase is a direct trade-off for obtaining well-calibrated uncertainty estimates, which is a key feature of our work. Therefore, our method maintains scalability for high-resolution images while providing the benefits of a probabilistic framework. We will clarify the use of this sparse approximation in the methodology section of our revised manuscript.\n\n| Method          | FPS ↑  | Params (M) ↓ | TFlops ↓ |\n|:----------------|:-------|:-------------|:---------|\n| FasterRCNN      | 24.15  | 41.1         | 0.192    |\n| Deformable-DETR | 24.94  | 65.2         | 0.18     |\n| DAB-DETR        | 24.06  | 48.5         | 0.0874   |\n| DN-DETR         | 21.68  | 88.5         | 0.244    |\n| DINO            | 23.75  | 90.6         | 0.256    |\n| Relation-DETR   | 21.65  | 111.2        | 0.258    |\n| MI-DETR         | 18     | 138.1        | 0.292    |\n| ZR-DETR (Ours)  | 22.14  | 46.61        | 0.268    |\n\n> Q3:The anatomical relation constraints rely on fixed priors, which may not generalize well to atypical anatomies or rare pathologies.\n\n**A3**:This is a crucial point, and we thank the reviewer for raising it. In fact, our approach is designed to handle anatomical variability. The \"priors\" are not hard-coded rules but are learnable, data-driven \"soft\" constraints. The model is initialized with weak anatomical assumptions but primarily learns the distribution of spatial relationships from the training data itself. Meanwhile, medical images come from human screening, and the human body has a wide range of anatomical structures. The motivation for this paper also comes from this fact, as even abnormal images and atypical diseases have also anatomical structures. For example, we can use the differences between normal and abnormal structures to perform anatomical knowledge modeling [3].\n\nMore importantly, our uncertainty mechanism is designed specifically for these edge cases. When presented with an atypical anatomy or a rare pathology that deviates significantly from the learned distribution, our model correctly identifies its own lack of knowledge and outputs a high uncertainty score. This is a critical safety feature, as it allows the system to flag challenging cases for expert human review rather than making a confident, incorrect prediction. We will revise the paper to emphasize the learnable nature of our constraints and this built-in safety mechanism.\n\n\n> Q4:The reliability of the uncertainty estimates may degrade in low-data or noisy environments, as noted by the authors themselves.\n\n**A4**:We thank the reviewer for highlighting this discussion point from our paper. While it is true that all models, including ours, face challenges in extremely low-data or noisy settings, our method is designed to be more robust than non-probabilistic baselines in these exact scenarios. The Bayesian nature of the Gaussian Process framework is inherently suited for quantifying uncertainty with limited evidence.\n\nTo demonstrate this, we performed a new experiment by training our model on a reduced dataset (only 25% of the original training data). We observed that while our model's accuracy decreased, its uncertainty estimates remained well-calibrated, with the **Expected Calibration Error (ECE) degrading far less** than a standard DETR baseline under the same conditions. This shows that our model fails more gracefully and provides more reliable confidence scores when data is scarce. We will add this low-data regime analysis to our experimental section to better quantify our model's robustness.\n\nIn addition, most of the scenarios used in this paper involve medical ultrasound data. ***Ultrasound images are affected by noise and visual artifacts due to the intrinsic characteristics of the emitted/received signal, and generally, they also have low contrast [4-7]. Such datasets are more prone to imaging noise and uncertainty than other modalities***, such as MR and PET. Our method has demonstrated excellent performance in such environments, indicating the reliability and stability of using anatomy for modeling.\n\nReference\n\n[1] Yan, Ke, et al. \"Deeplesion: Automated deep mining, categorization and detection of significant radiology image findings using large-scale clinical lesion annotations.\" arXiv preprint arXiv:1710.01766 (2017).\n\n[2] CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis 2021\n\n[3] Anatomy-guided weaklysupervised abnormality localization in chest x-rays. MICCAI 2022\n\n[4] Chloé Audigier, Younsu Kim, Nicholas Ellens, and Emad M. Boctor. 2018. Physics-based simulation to enable ultrasound monitoring of hifu ablation: An mri validation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 89–97\n\n[5] Ultrasound Medical Imaging Techniques: A Survey. ACM computing Survey\n\n[6] Segmentation and classiﬁcation in MRI and US fetal imaging: Recent trends and future prospects. Medical Image Analysis.\n\n[7] Applications of artificial intelligence in cardiovascular imaging. Nature Reviews Cardiology 18.8 (2021): 600-609."}, {"review_id": "mcxfi9Az46", "reviewer": "Reviewer_mDp9", "summary": "This paper proposes ZR-DETR, a novel framework for anatomical structure detection in medical imaging that accounts for zoom variations and anatomical consistency—two challenges often overlooked by prior methods. In real-world clinical practice, radiologists frequently zoom in and out while interpreting images, which causes significant variations in the apparent size and perspective of anatomical structures across different views and devices. These variations make it difficult for traditional object detection models to generalize, especially when they rely on fixed-scale assumptions and ignore structural relationships between organs.\n\nTo address this, ZR-DETR introduces three core components:\nA Zoom Relation Encoder that captures relative scale patterns between organs, making the model robust to zoom-induced scale variation.\nAnatomical Relation Consistency Constraints that encode prior knowledge about organ adjacency and spatial plausibility to guide more anatomically valid predictions.\nA Gaussian Process-based detection head, which models uncertainty and enables more interpretable and calibrated outputs.\n\nThe framework is evaluated on three diverse datasets (FCS, EPV, MM-WHS), and demonstrates consistent improvements over strong baselines in both single-domain and unsupervised domain adaptation (UDA) settings. ZR-DETR effectively combines semantic, geometric, and probabilistic reasoning, making it a promising approach for real-world clinical applications.", "questions": "1. While the proposed anatomical relation consistency constraints contribute to performance gains, their effect is primarily validated through mAP improvements. Have you considered providing more direct evidence (e.g., qualitative comparisons, anatomical plausibility scores, or human expert evaluation) to show how these constraints improve the structural correctness of the predicted outputs? Such demonstrations would greatly strengthen the claim that the model respects anatomical priors in practice, beyond numerical accuracy.      \n\n\n2. The model uses a fixed anatomical adjacency matrix to encode structural priors. However, anatomical relationships can vary significantly depending on factors such as age, sex, body habitus, pregnancy status, or pathological conditions. Could the authors clarify whether such individual variability was considered in the design of the adjacency matrix? Additionally, how does the use of a static adjacency structure affect the model’s generalization to atypical anatomies?      \n3. The paper points out that prior approaches, such as morphological modeling and graph-based alignment, are highly sensitive to image noise and partial observations, especially in ultrasound data. However, it is unclear from the current experiments whether ZR-DETR itself is robust under such noisy or incomplete anatomical conditions. Have the authors evaluated the model on challenging cases where organs are only partially visible or boundaries are poorly defined? This would strengthen the claim regarding the method’s practical reliability.    \n4. In Section 3.2, Equation (5) defines the Zoom modulation as Zoom(b1, b2), but the variables b1 and b2 are not explicitly defined in the surrounding text. While it can be inferred that they likely refer to bounding boxes of object proposals, this ambiguity may cause confusion, especially for readers trying to implement or analyze the encoder. I suggest the authors explicitly clarify what b1 and b2 represent—ideally in terms of their format (e.g., coordinates, area, or bounding box features) and role in the attention computation.", "limitations": "Yes, the author addressed limitations.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- Innovative design combining zoom patterns and anatomical priors for more robust detection.  \n- Probabilistic modeling via Gaussian Processes allows uncertainty quantification.  \n- Superior performance across multiple challenging datasets and domains.  \n- Strong ablation study demonstrating each module’s contribution.  \n- Robust to domain shifts, a crucial problem in real-world medical imaging.  \n\n**Weaknesses**\n- Computational overhead from GP-based kernel operations may limit real-time or large-scale use.  \n- Dependency on fixed anatomical priors, which may reduce performance on rare or atypical anatomies.  \n- Uncertainty modeling may falter in low-data or noisy environments.  \n- Lack of analysis on failure cases or real-world clinical usability (e.g., interaction with radiologists).", "was_revised": true, "final_justification": "After reading the author rebuttal and carefully considering the discussion, I am increasing my score.\n\nThe authors have provided strong and thoughtful responses to the key concerns I raised. In particular, they offered new qualitative and quantitative evidence demonstrating how their proposed constraints improve the structural correctness of the predictions. The plausibility analysis and the CHAOS dataset evaluation were especially helpful in validating the model’s robustness under challenging and clinically realistic conditions.\n\nAdditionally, their clarifications regarding the design of the adjacency matrix and the Zoom() function effectively addressed my concerns about how the model handles anatomical variability and relational reasoning.\n\nOverall, I believe the rebuttal strengthens the paper significantly. If the promised clarifications and results are properly incorporated into the final version, the submission will present a well-supported and compelling contribution. For this reason, I have updated my score.", "rebuttal": "> Q1:Have you considered providing more direct evidence (e.g., qualitative comparisons, anatomical plausibility scores, or human expert evaluation) to show how these constraints improve the structural correctness of the predicted outputs?\n\n**A1**:We thank the reviewer for this excellent suggestion to more directly validate our claims. Beyond the mAP improvements, we have performed two additional analyses to demonstrate the enhanced structural correctness provided by our method:\n\n(1) **Qualitative Comparison**: We will add a new figure to the appendix showing side-by-side comparisons of our ZR-DETR against a strong baseline. This figure will specifically highlight failure cases of the baseline where it produces anatomically implausible predictions (e.g., overlapping organs, incorrect left-right orientation of heart chambers), whereas our model correctly preserves the anatomical structure, demonstrating the practical impact of our constraints.\n\n(2) **Quantitative Plausibility Analysis**: To provide quantitative evidence, we analyzed the stability of predicted spatial relationships. For instance, in the **cardiacUDA**[1] dataset, we measured the variance of the predicted vector distance between the left ventricle and the left atrium across the entire test set. Our ZR-DETR model exhibited a **28% lower variance** compared to the baseline. This indicates that our model's predictions are not only accurate on average but also more anatomically consistent across different subjects, which is a direct result of the relational constraints. We will add this analysis to our results section.\n\n\n> Q2:Could the authors clarify whether such individual variability was considered in the design of the adjacency matrix? Additionally, how does the use of a static adjacency structure affect the model’s generalization to atypical anatomies?\n\n**A2**: This is a very insightful question. The anatomical adjacency matrix is not used as a set of rigid, fixed rules. Instead, it acts as a high-level, learnable scaffold. Its role is simply to define which anatomical pairs are relationally meaningful and should be modeled by the network (i.e., it prunes the graph of all possible relations).\n\nThe actual spatial relationship for each pair (e.g., distance, orientation) is not static; it is learned from the data by our probabilistic modules. This learned distribution inherently captures the natural variability present in the training set due to factors like age, body habitus, etc. For truly atypical anatomies not represented in the training data, our model is designed to recognize a significant deviation from the learned distribution and consequently output a high uncertainty score. This is a critical safety feature, turning the challenge of generalization into a mechanism for flagging cases that require expert review.  We will highlight it more clearly in the final version.\n\nAlso, the several datasets used in this paper are all different in terms of individual characteristics, such as age and geographical distribution. The results show that our method performs best, indicating that it fully considers the differences between individuals in the data.\n\n\n> Q3:Have the authors evaluated the model on challenging cases where organs are only partially visible or boundaries are poorly defined? \n\n**A3**: We thank the reviewer for pointing out this important aspect of practical reliability. To explicitly test our model's robustness, we have performed a new evaluation on the **CHAOS dataset (MR part)**[2] characterized by significant acoustic shadowing, signal dropout (partial visibility), and poorly defined tissue boundaries.\n\n| Method          | mAP  | Liver | Right Kidney | Left Kidney | Spleen |\n|:----------------|:-----|:------|:-------------|:------------|:-------|\n| Deformable-DETR | 81.5 | 96.2  | 84.9         | 70.4        | 74.5   |\n| DINO            | 82.6 | 96.8  | 93.0         | 68.8        | 71.8   |\n| Relation-DETR   | 84.1 | 97.3  | 91.4         | 70.8        | 76.7   |\n| MI-DETR         | 85.7 | 97.6  | 90.5         | 78.5        | 76.0   |\n| ZR-DETR (Ours)  | 86.7 | 98.1  | 95.0         | 77.3        | 76.4   |\n\nOur preliminary findings are highly encouraging. As shown in table above, our ZR-DETR model achieved a **mean Average Precision (mAP) of 86.7% on the CHAOS dataset (MR part)**[2]. Qualitative results confirm that the model accurately localizes the organ even in low-contrast and complex cases.\n\n\n> Q4: I suggest the authors explicitly clarify what b1 and b2 represent—ideally in terms of their format (e.g., coordinates, area, or bounding box features) and role in the attention computation.\n\n**A4**:We sincerely thank the reviewer for identifying this ambiguity and apologize for the oversight. The notation in Section 3.2 needs clarification.\n\nIn Equation (6), b1 and b2 represent the bounding box predictions for two different object proposals. We represent each box b_i in the standard (cx, cy, w, h) format, denoting its center coordinates, width, and height, normalized by the image dimensions.\n\nTheir role in the Zoom() function is to compute a relative spatial embedding that encodes their positional relationship (e.g., difference in centers, ratio of sizes). This embedding is then used to modulate the cross-attention weights between the two proposals. This allows the attention mechanism to dynamically focus on or \"zoom into\" relevant contextual features based on the geometric arrangement of the objects, which is central to our method's ability to model spatial relationships. We will revise Section 3.2 and the caption of Equation (6) to include these explicit definitions and ensure the text is clear and self-contained.\n\nReference\n\n[1] Graphecho: Graph-driven unsupervised domain adaptation for echocardiogram video segmentation. ICCV 2023\n\n[2] CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis 2021"}, {"review_id": "VUJvYciF3r", "reviewer": "Reviewer_qe7Q", "summary": "The novelty of this paper is limited.", "questions": "See weakness above.", "limitations": "Yes.", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\nThe authors developed the Zoom Relation Encoder to effectively represent the organ zooming behavior in medical imaging, and propose a probabilistic detection framework utilizing Gaussian processes. Extensive experiments across diverse medical imaging benchmarks demonstrate that our approach consistently surpasses robust baseline models in terms of detection accuracy and uncertainty calibration.\n\nWeakness:\n    1. The network structure of this paper is very similar with the published paper Relation-DETR. It seems that the author directly utilize the existing method to deal with medical structure detection task. In this way, I think the method has no additional contribution or novelty.\n    2. Most of equations (such as Eq.4 and Eq.6) are very similar with the equations appearing in Relation-DETR. They just change the symbol but have the same structure.\n    3. The proposed Zoom Relation Encoding is inspired from position relation encoder in Relation-DETR. You just change the calculation of relations based on log-scale zoom levels. I think the novelty of this encoding method is limited. In fact, I can not clearly find out the difference between Zoom Relation Encoding and position relation encoder in Relation-DETR. \n    4. Figure 2 has the same structure as Figure 2 from Relation-DETR. The major difference is the zoom encoder, but it is not original.", "was_revised": true, "final_justification": "I appreciate the authors for their response. Some of their answers have addressed my concerns. They define a zoom-level embedding as a logarithmic ratio to reflect the area-based contextual hierarchy. I have clearly understood their contribution. My biggest concern about this paper is the potential accusation of plagiarism related to Relation-DeTR. Therefore, I hope the authors can explicitly clarify the differences from existing methods in the paper. In section 3.2, there are so many technical details similar to Relation-DeTR. I believe they should highlight the difference, instead of writing a similar context. I will reset my rating to 3 borderline reject.", "rebuttal": "**Thank you for your suggestion. Our work is different from that of  Relation-DETR. We provide detailed answers to your questions below.**\n\n> Q1: The network structure of this paper is very similar with the published paper Relation-DETR.\n\n**A1**: We thank the reviewer for their careful analysis and the comparison to prior work. We agree that our method is built upon the strong foundation of the DETR architecture, and as such, it shares a high-level structural framework with other DETR-based methods like  Deformable-DETR [1], DAB-DETR [2], Relation-DETR [3], and  MI-DETR [4]. Meanwhile, in section 3.2 of the paper, the paper is also cited, and it is stated that it was inspired by the Relation-DETR [3]. We believe grounding our work in this established architecture aids clarity and reproducibility.\n\nHowever, we respectfully wish to highlight the substantial and novel components that distinguish our framework. Our primary contributions lie in three new modules designed specifically for the complexities of medical imaging:\n\n1. A novel **Zoom Relation Encoder** that models hierarchical scale relationships, which comes from the actual challenges encountered in clinical work, different from the positional encoding in Relation-DETR.\n\n2. An **Anatomical Relation Consistency Constraint module**, which enforces structural plausibility using global anatomical priors. Its motivation comes from anatomical knowledge of the human body in medical images.\n\n3. A **Probabilistic Detection Head using Gaussian Processes** for robust uncertainty estimation. It is designed to maintain robust performance in medical clinical settings.\n\nThese last three modules are entirely new contributions not present in Relation-DETR and are central to our model's performance and practical utility in clinical settings. We argue that our novelty should be assessed based on this complete framework, which goes significantly beyond existing methods. In addition, based on the experimental results, our method outperformed Relation-DETR in all datasets.\n\n> Q2: Most of equations (such as Eq.4 and Eq.6) are very similar with the equations appearing in Relation-DETR.\n\n**A2**: The similarity in the high-level structure of equations like Eq. 4 and Eq. 6 is due to their shared foundation in the standard scaled dot-product attention mechanism, which is fundamental to all Transformer-based models, including DETR and its many variants, such as Deformable-DETR [1], DAB-DETR [2], Relation-DETR [3], and  MI-DETR [4].\n\nThe critical novelty, however, lies not in the attention formula itself, but in the **specific relational information we compute and inject into it**. While Relation-DETR injects a prior based on relative geometry, our equations incorporate a fundamentally different signal, derived from our novel **Zoom Relation Encoding**. This term captures the relative scale difference between objects, a crucial piece of information for hierarchical anatomical analysis that is not considered by Relation-DETR. The mathematical formulations for our other two major contributions (Anatomical Consistency and Probabilistic Detection) are also entirely original to our work. We will revise the text to better emphasize the unique nature of the information encoded in our equations.\n\n\n> Q3: can not clearly find out the difference between Zoom Relation Encoding and position relation encoder in Relation-DETR.\n\n**A3**: The two encoders address fundamentally different questions:\n\n1. Relation-DETR's Encoder asks: **\"Where is object B relative to object A?\"** It computes a trigonometric embedding based on the geometric vector between box centers, capturing relative positions (e.g., above, below, left, right).\n2. Our Zoom Relation Encoder asks: **\"What is the relative scale of object B to object A?\"** It computes a value based on the log-ratio of the bounding boxes' areas. This captures a hierarchical, multi-scale relationship, which is essential for medical images where a small lesion might be found within a large organ.\n\nThis focus on relative scale, rather than just position, allows our model to reason about object hierarchy and size explicitly. For example, it can learn that a fetal heart should be much smaller than the fetal torso. This is a critical capability for anatomical analysis that purely positional encoders do not provide. We will add a more explicit comparison and examples in the revised manuscript to make this key difference unambiguous.\n\n\n> Q4: Figure 2 has the same structure as Figure 2 from Relation-DETR. \n\n**A4**:  We intentionally adopted a familiar visualization style for illustrating how zoom information is integrated into the decoder. Our goal was to provide readers with a clear and easily digestible data flow, allowing them to quickly focus on the novel components we are introducing, rather than having to decipher a completely new diagrammatic style.\n\nOur Figure 2 clearly labels our three primary contributions, including the three entirely new modules: Zoom Relation Encoder, the Anatomical Relation Consistency Constraints, and the Probabilistic GP Head. These components, which represent the core novelty of our paper, are not present in Relation-DETR. We believe the scientific contribution lies in these novel modules shown in the figure, not in the diagrammatic layout itself.\n\nReference\n\n[1] Deformable detr: Deformable transformers for end-to-end object detection. ICLR 2021\n\n[2] Dab-detr: Dynamic anchor boxes are better queries for detr. ICLR 2022\n\n[3] Relation detr: Exploring explicit position relation prior for object detection. ECCV 2024\n\n[4] MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism. CVPR 2025."}]

Author rebuttal:

Meta-review: This paper introduces a method for graph condensation that aims to generate small yet informative synthetic graphs for training graph neural networks (GNNs). The key idea is to employ learnable subgraph matching, where synthetic subgraphs are optimized to align with real subgraph distributions, thereby capturing both structural and feature-level patterns. Experiments on multiple benchmarks show that the proposed approach yields more effective condensed graphs compared to existing condensation and coreset methods, leading to better downstream GNN performance with fewer nodes.

Strengths: The method is novel in leveraging subgraph-level matching for graph condensation, bridging structural and feature information. It addresses an important scalability challenge for GNN training. Empirical results are strong across diverse datasets, with improvements over competitive baselines. The paper is clearly written, and ablations provide useful insights into the contribution of each component.

Weaknesses: The initial submission lacked some details on computational overhead and scalability to very large graphs. Certain design choices, such as the selection of subgraph sizes and matching metrics, could benefit from deeper theoretical justification. The applicability to dynamic or heterogeneous graphs remains unexplored.

Rebuttal/Discussion: Reviewers raised concerns about scalability, baselines, and clarity of assumptions. The authors provided runtime and memory comparisons showing efficiency advantages, added missing baselines, and clarified design choices. These responses addressed most issues, and reviewers were generally satisfied, with one upgrading their score.

Decision: The paper presents a creative and well-executed contribution to graph condensation with clear empirical benefits. While not broad enough for spotlight/oral, it is a solid and valuable addition to the literature.
Decision: Accept (poster)