AbdCTBench: Learning Clinical Biomarker Representations from Abdominal Surface Geometry
Authors: Muhammad Ahmed Chaudhry
Suhana Bedi
Pola Lydia Lagari
Brian T Layden
William Galanter
Ayis Pyrros
Sanmi Koyejo
Venue: ICLR 2026 Poster (2026)
Keywords: computer vision for healthcare
radiology
Computed Tomography (CT)
vision transformers
CNNs
Primary area: datasets and benchmarks
TL;DR: AbdCTBench is the first benchmark showing that abdominal surface geometry alone can be predictive of clinically meaningful biomarkers. We release the dataset to enable progress toward scalable, non-invasive cardio-metabolic risk screening

Abstract: Body composition analysis through CT and MRI imaging provides critical insights for cardio-metabolic health assessment but remains limited by accessibility barriers including radiation exposure, high costs, and infrastructure requirements. We present AbdCTBench, a large-scale dataset containing 23,506 CT-derived abdominal surface meshes from 18,719 patients, paired with 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composition, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, Swin Transformer-Base), demonstrating that models can learn robust surface-to-biomarker representations directly from 2D mesh projections. Our best-performing models achieve clinically relevant accuracy: age prediction with MAE 6.22 years (R²=0.757), mortality prediction with AUROC 0.839, and diabetes (with chronic complications) detection with AUROC 0.801. Notably, smaller architectures consistently matched or surpassed larger models, while medical-domain pre-training (RadImageNet) and self-supervised pre-training (DINOv2) showed competitive but not superior performance. AbdCTBench represents the largest publicly available dataset bridging external body geometry with internal clinical measurements, enabling future research in accessible medical AI. We plan to release the dataset, evaluation protocols, and baseline models to accelerate research in representation learning for medical applications, immediately following the review period.

Published as a conference paper at ICLR 2026
ABDCTBENCH: LEARNING CLINICAL BIOMARKER REPRESENTATIONS FROM ABDOMINAL SURFACE GE- OMETRY
Muhammad Ahmed Chaudhry
Suhana Bedi
Pola Lydia Lagari University of Illinois Chicago
Stanford University
Stanford University
Brian T Laydenn
William Galanter University of Illinois Chicago
Ayis Pyrros University of Illinois Chicago
Sanmi Koyejo
University of Illinois
Stanford University
Crbana-Champpii
ABSTRACT
Body composition analysis through CT and MRI imaging provides critical in- sights for cardiometabolic health assessment but remains limited by accessibil- ity barriers including radiation exposure, high costs, and infrastructure require- ments. We present AbdCTBench, large-scale dataset containing 23,506 CT- derived abdominal surface meshes from 18,719 patients, paired with 87 comor- bidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composi- tion, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet- 18/34/50, DenseNet- 121, EfficientNet-BO. ViT-Small, and Swin Transformer- Base), demonstrating that models can learn robust surface-to-biomant tations directly from 2D mesh projections. Our best best-performing models achieve clinically relevant accuracy: age prediction with MAE 6.22 years =07=0,,57) mortality prediction with AUROC 0.839, and diabetes (with chronic complica- tions) detection with AUROC 0.801. Notably, smaller architectures consistently matched or surpassed larger models, while medical-domain pretraining (RadIm- ageNet) and self-supervised pretraining (DINOv2) showed competitive but not superior performance. AbdCTBench represents the largest publicly available dataset bridging external body geometry with internal clinical measurements, en- abling future research in accessible medical AI. We release the dataset, eval- uation protocols, and baseline models to accelerate research in representation learning for medical applications, at peets://sstais /ssaarrrrrrrrrrr lab. wab.giithut www.hittuub..... io/ AbdCTBench/
represen-
INTRODUCTION
Body composition analysis has emerged as critical avenue for advancing preventive and diagnostic medicine, offering valuable insights into cardiometabolic health (Amato et al., 2013) Rosenquist et al. 2013). While traditional metrics such as body mass index (BMI) and waist circumference are widely used, they fail to differentiate between metabolically active visceral adipose tissue (VAT), intramuscular fat infiltration, and organ-specific pathologies all critical for cardiometabolic risk stratification (Lee et al.] 2018; [Sweatt et al.) 2024] [Therkelsen et al., 2013). This limitation has led the adoption of advanced imaging biomarkers derived from computed tomography (CT) and magnetic resonance imaging (MRJ), which provide quantitative assessments of tissue composition with unprecedented precision (Thomas al. 2025).
However, the clinical utility these modalities remains limited by accessibility barriers. CT ex- poses patients to ionizing radiation, precluding repeated use, while MRI is costly and has limited availability. Both modalities require specialized infrastructure and trained radiologists, creating
"Corresponding author. Email: mahhmddhesttanford.edd

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Published as a conference paper at ICLR 2026
bottlenecks in resource-constraing settings and perpetuating health disparities. These limitations underscore the need for alternative approaches that provide access to clinically useful biomarkers while overcoming accessibility barriers.
Figure 1: Sample 2D abdominal surface meshes from the AbdCTBBnch dataset. These CT-derived surface geometries demonstrate the range of external anatomical features used to predict internal body composition biomarkers without radiation exposure. Complete biomarker details for these images are provided the Appendix A.
To address this challenge, we present AbdCTBench, the first-of-its-kind dataset of 2D surface meshes derived from formerly conducted abdominal CT scans. AbdCTBench is carefully curated to enable development of computer vision techniques for learning representations from surface ge- ometry predict variety of clinically useful biomarkers. We fine-tune an array of state-of-the-a models and benchmark their performance on the task of predicting the associated biomarkers. Our models are trained on detailed internal body composition data from CT scans and reflect the feasi- bility of effectively capturing the predictive signal by using only external body measurements and surface geometry at inference. Model predictions can be obtained simply by providing 2D surface mesh images as input, aimed at bridging the gap between accessible consumer technology.
clinical imaging and widely
By validating against the gold standard of CT-derived biomarkers, AbdCTBench can facilitate the development of reliable models that ultimately eliminate the need for CT scans in routine screening and monitoring through surface mesh imagery. Recent breakthroughs in consumer-grade depth sens- ing, such as L.DAR-cnables devices like the iPhone, offer viable alternatives for body composition assessment (Oberhofer al. 2024 Zamotsin et al., 2022 Boczar al. 2024 Vasic et al. [024] and provide accessible methods for generating the surface meshes required by these models. While early implementations struggle with complex torso geometries (Galaaoui al. (025). iterative scanning protocols and machine learning-based mesh reconstruction algorithms are rapidly closing the fidelity gap with clinical CT. Meanwhile, imaging foundation models trained on extensive radiographic data demonstrate the feasibility of extracting biomarkers from various imaging modalities. When applied 2D surface scans, such models could learn associations between external surface geometry and internal composition patterns.
Our research represents transitional step this evolution with two main contributions:
We curate AbdCTBench, a dataset of 23,506 CT-derived abdominal surface mesh images from 18,719 unique patients. To our knowledge, AbdCTBench is the first and largest publicly available dataset its kind. Our dataset release includes the 2D surface mesh images (depth map projec- tions), 3D surface meshes obtained from the CT DICOM image series, the complete DICOM-to- STL-to-PNG processing pipeline, and the CTT-derieed biomarker extraction code. Our complete Dataset Release Statement is provided in Appendix [A.2]
2. We benchmark a variety of computer vision architectures on predicting CT-derived ground truth biomarkers available in AbdCTBench, requiring models to learn the relationship between exter- nal abdominal geometry and internal CT-derived biomarkers. We also release the pre-traine pre-trained model checkpoints for all architectures benchmarked.
Once validated, these models could operate solely on surface-derived 2D meshes without requiring CT imaging. This approach could transform biomarker accessibility by providing individuals, clin- icians, and rescarchers with actionable physiological insights through non-invasive. radiation -fee surface scans, potentially enabling broad, low-cost, and scalable health screening tools for early disease risk detection.
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RELATED WORK
2.1 MEDICAL IMAGE ANALYSIS BENCHMARKS
Medical imaging benchmarks have played an important role in advancing computer vision methods for healthcare. Large-scale datasets such as ImageNet inspired challenges (e.g. CheXpert (Irvin et al., 2019), MIMIC-XRR (Johnson et al. 2019)) have provided valuable testbeds for algorithm development and reproducibility. These resources are typically derived from high-fidelity imaging modalities and clinician-verffied verified ground truth labels such radiologist reports discase codes. While these benchmarks have accelerated progress in medicine, they remain tied to modalities like CT. MRI, or X-ray that require specialized infrastructure and are often inaccessible for population- scale screening.
In contrast, external body shape analysis has primarily been explored in non-clinical domains such as human pose estimation (Cao et al. 2019) or anthropometric studies. To date, no benchmark has systematically linked external abdominal surface geometry with internal, CT-derived biomarkers scale. AbdCTBench fills this gap by bridging the rigor of medical imaging benchmarks with the accessibility of surface-based imaging, enabling a new class of methods for non-invasive biomarker assessment.
2.2 ARCHITECTURE EVALUATION IN MEDICAL IMAGING
Benchmarking diverse neural architectures has been central medical image analysis research. Early convolutional neural networks (CNNs) demonstrated success on tasks such as tumor segmen- tation (Ronneberger et al. 2015), while later work has shown the advantages transformers (Doso- vitskiy et al.) 22221a] Chen et al. (221aa) and hybrid -NN-trrnsformer architectures for capturing global context in medical images. More recently, foundation models pretrained on massive radiol- ogy corpora (e.g., RadImageNet (Mei al. 2022a)) have highlighted the benefits of transfer learning for downstream tasks.
Existing architectural studies, however, primarily benchmark models imaging modalities that capture internal anatomy directly (CT, MRI, X-ray). By contrast, AbdCTBench evaluates the abil- ity of architectures to infer internal body composition from external abdominal surface meshes. This task differs fundamentally from conventional medical imaging because the predictive signal is indirect, requiring models to learn associations between geometry and physiology. As such, AbdCTBench provides new arena to assess whether architectural advances spanning CNNs, transformers, and emerging vision models generalize to this novel, indirect inference problem.
3 ABDCTBENCH DATASET
AbdCTBennh
Surface images with CT-derived ed biomarkers, dingnodi codes, codes, lab values
Ci scams genorate DICOM seriss
.TL
ORC AR calratates CT-hismrkkes BICOM csmortte to STL, files and aad be nudur artac mosk inage
Patlent Medical Hllttryy paired Abol! Bench
BdCCBunel used to Benchmark CNN and Tt Trandfrnct based archicclures
Figure 2: AbdCTBench dataset overview showing the pipeline from CT scans surface mesh extraction and biomarker prediction.
3.1 DATASET COLLECTION AND CURATION
AbdCTBench is comprehensive dataset derived from 23,506 abdominal CTs of 18,719 patients (RS 1.26 scans per patient), representing one of the largest CT-derived biomarker datasets for abdominal composition analysis. The data collected from all available CT scans conducted at facilities of privately-owe -owned healthcare company. This included CT scans conducted from August 11, 2003, to September 9, 2021, under IRB approval from the University of Wisconsin (Protocol: "Opportunistic
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CT Screening"). The utilization of all available CT scans allowed the creation of the largest possible dataset available, but may introduce implicit biases in the data which we discuss further in section
7 Following collection, processing proceeded in two parallel phases: surface mesh rendering and CT-derived biomarker calculation.
For surface mesh rendering, DICOM image series were converted to stere--lithograhhy (STL) files, then to 2D PNG images of size 384 x 384 (Figure I via Py ViVist (Sullivan & Kaszynski] 2019). The conversion pipeline consists of three sequential stages: volume processing applies optional shrinking and anisotropic smoothing to prepare the data; surface extraction generates 3D triangular meshes us- ing VTK's contour filter, which are refined through mesh cleaning and smoothing operations before being exported as binary STL files; and finally, the STL to 2D image conversion renders each mesh into standardized PNG images with fixed camera positioning and mesh smoothing. Further techni- cal details about the DICOM to STL and STI. to PNG conversions are provided in the Appendix A.3 For biomarker calculation, DICOM series were processed by OSCAR (Pickhardt et al. 2020), which creates segmentation masks to calculate metrics at vertebral levels (LI-L5, TI0-TI2) and organ-specific regions (liver, spleen, kidneys, aorta). Key measurements include bone mineral den- sity, adipose tissue distribution, musele composition, organ volumes, and calcium scoring metries, yielding 16 biomarkers measuring body composition at various anatomical levels.
The CT-derived biomarkers were then paired with 31 diagnosis (ICD-10) codes, 87 hierarchical condition category (HCC) comorbidity labels (groupings of ICD-10 codes (Amerigroup) 2019)) and 2 longitudinal lab values (HbAlc and C-reactive protein) from patient medical history. The dataset was then processed for HIPAA Safe Harbor (U.S. Department of Health and Human Services, Office for Civil Rights 2012) compliance, removing PII for safe public release, and we performed a 70/20/10 split at the patient ID level for train, validation, and test sets to prevent data leakage. All hyperparameter tuning and model selection procedures used only the train and validation sets, and the test set was held out only to perform the final evaluation for the reported results. The resulting dataset integrates quantitative CT biomarkers with clinical outcomes, comorbidity codes, and demographic information for comprehensive cardiometabolic health analysis and architecture benchmarking.
3.2 DATASET STATISTICS AND ANALYSIS
AbdCTBench represents a diverse patient population with mean age 55.3 years (SD: 16.51). For HIPAA compliance, ages > 90 were categorized as (90+), with 298 such cases excluded from age statistics. The dataset shows balanced sex distribution (56.8% female. 43.2% male) and significant clinical diversity, with high prevalence of: essential hypertension (53.7%), Type 2 Diabetes (44.6%), impaired glucose tolerance (38.0%), tobacco use (26.8%), and Myocardial Infarction (MI, 23.1 %)..
HCC comorbidity burden analysis reveals substantial heterogeneity, with patients carrying an av- erage of 1.8 HCC conditions (max: 24 for single patient). Most prevalent HCC codes include: HCC-108 (Vascular Disease; 22.6%). HCC-19 (Diabetes without complications; 13.0%), and HCC- 12 (Breast, Prostate, and other Cancers; 10.9%). Overall mortality rate is 11.4%.
Primary CT-derived biomarkers include: Calcium Scoring Abdominal Agatston score (mean: 1200.9 + 3126.5), kidney median Hounsfield units (mean: 90.0 + 58.8HU), spleen median Hounsfield units (mean: 82.1 + 37.2HU), spleen volume (mean: 223.9 + 127.2cm"), and com- prehensive adipose tissue analysis at vertebral levels (LI-L5, T10-T12). Comprehensive dataset statistics are provided in the Appendix[A.4
ABDCTBENCH BENCHMARK
From AbdCTBench, we curate 10 biomarker prediction tasks from 2D surface mesh images. We design single-target learning framework to benchmark selected architectures on biomarker predic- tion. The goal is twofold: (i) design a standardized evaluation framework for comparing computer vision architectures across diverse biomarker prediction tasks, and (ii) benchmark foundation mod- els for CT-derived biomarker prediction using surface geometry representations from 2D images. The biomarker prediction tasks are as follows:
Mortality prediction (1 (11.4%): binary classification for patient death during follow-up. 108 (Vascular Disease; 22.6%) binary classification for HCC-108 presence sean time.
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HCC-11 (Breast, Prostate, & other Cancers; 10.9%).: binary classification, scan time.
HCC-96 (Cardiac Arrhythmias: 9.0%)) binary classification, at scan time.
HCC-18 (Diabetes with Chronic Complications: 8.3%). binary classification,
time.
HCC-111 (Chronic Obstructive Pulmonary Disease; 7.1%): %): binary classification, at scan time.
Calcium Scoring Abdominal Agatston: binary classification 1000 (Janjua et al.. 2021 (2..2%)
Myocardial Infarction (MI; 23.1 %): binary classification for previous myocardial infarction.
Type 2 Diabetes (44.6%); binary classification for presence scan time.
Age (mean 55.3): regression for patient age at scan time.
4.1 ARCHITECTURE SELECTION
We selected 7 representative architectures spanning different families to ensure comprehensive cov- erage of modern computer vision approaches for medical image representation learning. CNN Architectures: Four models (representing different design philosophies were included.
ResNet-18/33 (He et al. 2016): Residual networks with skip connections, for strong baseline
performance with efficient parameter usage (4-6GB and 6-8GB GPU memory respectively)
DenseNet-121 (Huang et al.
2017): Densely connected networks maximizing feature reuse through concatenation-basee connections (8-10GB GPU memory)
EfficientNet-B0 (Tan & Le 2019): Compound scaling approach balancing depth, width, and resolution for optimal efficiency (6-8GB GPU memory)
Vision Transformers: ViT-Small (DINOv2) (Oquab et al. 2024 (8- 1--BBB GPU memory) evalu- ates self-supervised pre-training effectiveness with vision transtormers on medical imaging tasks. DINOv2's self- supervisd pre-training has shown superior performance compared to supervised ImageNet pre-training on various downstream tasks. On the other hand, Swin Transformer-Bas (Liu et al. 2021) (12- 12BI GPU memory) represents hierarchical vision transformers with shifted window attention, providing an alternative and modern transformer architecture for comparison.
Medical-Speeific Architecture: ResNet-50 (RadImageNet) (Mei et al. 2022b) (6-8GB GPU memory) represents -specicc pre-training, utilizing RadImageNet weights trained on medi- cal images to assess medical domain knowledge benefits.
4.2 STANDARDIZED TRAINING PROTOCOL
For fair and reproducible comparison across architectures, we establish standardized training pro- tocol for all models, designed based on best practices from the medical imaging literature.
Optimization Configuration: All models the AdamW optimizer (Loshchilov & Hutter 2019 with weight decay x 10 10-4 and cosine annealing learning rate scheduling. Adam W is the optimizer of choice for various medical imaging tasks (Chang) 2024: Mortazi al. [203). We evaluate three learning rates: x 10 -5 1 x 10-4, and 1 x 10 to capture different optimization regimes and
ensure robust performance across architectures.
Training Hy pyperparameters: Batch size of 16 balances memory efficiency with gradient stability. All models train for 100 epochs with early stopping based on validation performance (patience: 10 epochs). Dropout of 0.2 prevents overfitting, as established in medical imaging literature (Wang & Huang 2024 Maruyama al., 2025 [Adebayo & et al., (025).
Loss Function: We standardized across all models to use binary cross-entropy loss with logits for binary classification tasks, and mean squared error loss for regression tasks.
Fine-tuning Strategy: All models use full fine-tuning to maximize performance. Pre-trained weights are loaded from ImageNet (CNN models), DINOv2 (VTT), or RadImageNet (medical- specific ResNet-50), with final classification layers replaced with task-specific heads.
Data Augmentation: A standardized augmentation pipeline is applied to models using pre- training weights with slight modifications for medical imaging tasks. For ImageNe//DINO2 pre- trained models: random horizontal flips (p=0.3), geometric augmentations including random rota- tions (=7.5) and less aggressive crops (0.9-1.0 original size) with aspect ratio 0.8 (p=0.6). For grayscale images, reduced intensity color augmentations (halved brightness/contrant shift, p=0.4) are applied. Images are then converted 3-channel by repeating tensors along channel dimension. For
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normalization, ImageNet models ImageNet mean (0.485, 0.456, 0.406) and std (0.229, 0.224, 0.225); DINOv2 models use CT-derived means (0.55001191, 0.55001191, 0.55001191) and stds (0.18854326, 0. 088885332, 0.18554366 (Pyrros al. 2023 For RadImageNet (medical imaging specific), augmentations are more conservative: random horizontal flips (p=0.2, reduced from 0.3), random rotations (=5, reduced from +7.5), less aggressive crops (0.95-1.0 original size, reduced from 0.9-1.0), narrower aspect ratio (0.9-1.1, reduced from 0.8-1.2), applied with p=0.4 (instead of 0.6). Color augmentations: color jitter for brightness/contrast with reduced intensity 0.3 (instead of 0.5), applied with p=0.3 (instead of 0.4). Images are then similarly converted to 3-channel and normalized using ImageNet mean/std as recommended by Mei et al. (2022b).
4.3 SINGLE- TARGET LEARNING FRAMEWORK AND CLASS IMBALANCE HANDLING
Our benchmark focuses on single-target learning to establish clear performance baselines for biomarker prediction tasks, allowing direct comparison of architectures without multi-task learn- ing dynamics.
Architecture Adaptation: CNN models use direct classification heads; Vision Transformers em- ploy CLS token classification. For each, the final layer replaced with a task-specific head out- putting the appropriate number of classes continuous values.
Evaluation Strategy: Each architecture evaluated independently on each biomarker task, allow- ing comprehensive analysís of architectural strengths across different prediction types. This ap- proach provides clear insights into which architectures excel at specific biomarker prediction types.
Further, described in section 3 the dataset exhibits severe class imbalance. significantly impacting model performance and evaluation. We implement all the strategies described below to address this challenge as standard training protocol across all models and architectures:
Inverse Frequency Weighting: Applied to the loss function, where class weight calculated as the inverse of class frequency the dataset.
Balanced Batch Sampling: During training, balanced batch sampling ensures each batch con- tains approximately equal representation from all classes, preventing model domination by majority classes.
Threshold Optimization: For binary classification tasks, classification thresholds optimized using Fi-score on the validation set, searching the range [0.1, 0.9] with 9 discrete steps. This ensures optimal performance for imbalanced datasets where default 0.5 thresholding may be suboptimal.
5 EXPERIMENTAL SETUP
All experiments were implemented in PyTorch with CUDA 12.4 support. We utilized the timm library for Vision Transformer implementations and torchvision for CNN architectures. Experiments are conducted on NVIDIA GeForce RTX 2080 Ti or NVIDIA H200 GPUs with 10 GPUs available for parallel execution. Each model is allocated sufficient memory based expected requirements (4-16GB range), ensuring no memory-related performance degradation.
5.1 EVALUATION METRICS
Our evaluation framework employs task-specific metrics for comprehensive assessment of model performance across different biomarker types:
Binary Classification Metrics: AUROC (area under the receiver operating characteristic curve, providing dineshold-independdee performance assessment), F1-Score, Precision/Recall, and Speci- ficity. Given clinical relevance, the best model is identified based on highest AUROC. Since FI- Score, Precision, Recall, and Specificity threshold dependent, the F1-score optimizing threshold selected from the validation set, then used to calculate performance metrics on the test set.
Regression Metrics: Mean Absolute Error (MAE) and Mean Squared Error (MSE). Since age
prediction is our only regression task, the best model selected based on lowest MAE, allowing interpretation of prediction quality in terms of absolute difference in years from true patient age.

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Statistical Significance: Results reported with bootstrapped 95% confidence intervals to assess reliability of performance differences. The bootstrapping was performed with 1000 samples each of size equal to the test set, using simple random sampling with replacement. Samples may be excluded in case of low label diversity (e.g- both classes not present for the binary classification tasks). The 95% confidence intervals were calculated using the percentile method from the distribution of bootstrap statistics for each metric of interest.
RESULTS AND ANALYSIS
We evaluated single-biomarker predictors across the seven architectures, using the best checkpoints selected on validation and report test set performance. For the regression task (age), EfficientNet- BO achieved the best error (MAE 6.22), with ResNet-50 (RadImageNet, MAE 6.34) and ViT-Small (MAE 6.47) close behind. All models substantially outperformed naive baseline (R² 0.719, reported in Appendix A.5), indicating strong representation learning from abdominal surface geom- etry for age estimation. For binary targets, AUROC varied by biomarker and architecture:
Calcium Scoring Abdominal Agatston: ResNet-34 achieved the best AUROC of 0.848, with
DenseNet-121 at 0.847
Myocardial infarction (MI): Swin Transformer-Bass achieved the best AUROC of 0.742, with EfficientNet-BO at 0.732 trailing closely
Mortality Prediction: ResNet- 18 with an AUROC of 0.839 was the best performing. EfficientNet-
BO at 0.830 and Swin transformer-Bi
at 0.828 were close behind
Type 2 Diabetes (T2D): Ne achieved the best AUROC of 0.742, with EfficientNet-BB and Swin Transformer- Base at 0.740
Table 1: Results for non-HCC biomarkers by architecture the test set. AUROC is reported for the binary classification tasks and MAE is reported for Age prediction (regression task). Bootstrapped 95% CIs reported in parentheses.
Architecture
Age (MAE)
Calcium Score
MI
Mortality
T2D
Naive Baseline
13.16
0.500
0.500
0.500
0,500
(12.79-13.57)
DenseNet-121
6.769
0.847
0.730
0.823
0.728
(6.551-6.994)
(0.829-0.8 0,863)
(0.703-0.752) 0,752)
(0.800-0.8) 0.845)
(0.709-0.750)
EfficientNet-BO
6,223
0.847
0.732
0,830
0.740
(6.016 6.422)
(0.829 00,64)
(0.708 0. 0,754)
(0.805-0.55 0.852)
(0.720-0.761)
ResNet-18
6.472
0.843
0.729
0.839
0.735
(6.264-6.678)
(0.825-0.859)
(0.705-0.752)
(0.816-0.861)
(0.714-0.756)
ResNet-34
6.486
0.848
0.731
0.825
0.742
(6.284-6.692)
(0. 1..70600.733
(0.799-0.848)
(0.722-0.762)
ResNet-50
6,341
0.833
0.716
0.810
0.733
(RadImageNet)
(6. (6.15-6.)332)
(0.815-0.849)
(0.693-0.739)
(7784008844)
(0.714-0.753)
ViT-Small
6,465
0.829
0.732
0.811
0.735
(DINOv2)
(6.260-6.684)
(0.809-0.846)
(0.707-0.754)
(0.785-0.836)
(0.714-0.755)
Swin
6.540
0.845
0.742
0.828
0.740
Transformer-Be
(6.338-6.756
(0.828 0.862)
(0.718 0.763)
(0.803 0.851)
(.720-0.759)
HCC-108 (Vascular Disease): best AUROC of 0.768 as achieved by Swin Transformer-Base
with 18 0.763, and EfficientNet-BOO 0.753
HCC-111 (Chronic Obstructive Pulmonary Disease, COPD): best AUROC of 0.769 again achieved by ResNet-18, with Swin Transformer-Bi trailing at 0.765
HCC-12 (Breast, Prostate, and other Cancers): ResNet-34 achieved the best AUROC of 0.591. Most performance numbers clustered near chance-level (0.571-0.591) across architectures. We believe this reflects both limited biological plausibility and label design: HCC-12 aggregates multiple, heterogeneous cancer types with differing and often weak relationships to obesity and abdominal body composition. Further, the timing of the HCC code relative to the CT scan can span pre-diagnosis, active treatment, and long-term survivorship. Taken together, these factors likely attenuate any subtle signal from the surface geometry, consistent with our interpretation that abdominal surface geometry is primarily predictive of cardio ceddio-metabolic and musculoskeletal biomarkers rather than oncologic comorbidities

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Table 2: Results for HCC code biomarkers by architecture the test set. All biomarkers report AUROC. Bootstrapped 95% CIs are reported in parentheses.
Architccture
HCC-108
HCC-111
HCC-12
HCC-18
HCC-96
Naive Baseline
0.500
0.500
0.500
0.500
0.500
DenseNet- 121
0.740
0.716
0.587
0,766
0.757
(0.720-0.77) 0.760)
(.88-00.799)
(0.735 (..35--0.997)
(0.723-0.78))
EfficientNet-BB
0.753
0.747
0,586
0,789
0,763
(0.732-0.775)
(0.708-0.782)
(0.550-0.623)
(0.760-0.816)
(0.732-0.790)
ResNeet118
0.763
0.769
0,580
0.799
0.760
(0.742-0.784)
(0.734-0.802)
(0.544-0.6144
(0.770-0.828)
(0.727-0.791)
ResNet-34
0.749
0.766
0.591
0,775
0.728
(0.729-0.769)
(0.730-0.799)
(0.557-0.624)
(0.746-0.805)
(0.694-0.761)
ResNet-50
0.718
0.739
0,571
0,782
0.738
(RadImageNet)
(0.704-0.774)
(0.536-0.607)
(0.754-0.812)
(0.705-0.769)
ViT-Small
0.743
0.760
0.576
0.785
0.757
(DINOv2)
(1723-0.764)
(0.727-0.793)
(.642-0.600)
(0.755-0.817)
(0.721-0.7 120.7888)
Swin
0.768
0.765
0,580
0.801
0.770
Transformer-Base
(0.749-0.788)
(0.732-0.796)
(0.545-0.616)
(0.776-0.828)
(0.739-0.798)
HCC-18 (Diabetes with Chronic Complications): best AUROC of 0.801 was achieved by Swin Transformer-Base It is noteworthy that this AUROC is higher than the highest AUROC of 0.742 for Type 2 Diabetes prediction as achieved by ResNet-34. This difference may be reflective of the greater predictive capacity of representations from surface geometry for diabetes with chronic complications more so than simply the presence of Type Diabetes
HCC-96 (Cardiac Arrhythmias): best AUROC of 0.770 achieved by Swin Transformer-Base with EfficientNet-BOO at 0.763, ResNet-18 18 at 0.760, and ViT-Small at 0.757
Across biomarkers, smaller-to-midsized CNNs (ResNet-18/34, EfficientNet-B0 consistently matched surpassed larger ResNet-50 models. ViT-Small with DINOv2 pretraining showed com- petitive performance, often ranking in the top 2-3 architectures but not achieving the best results on any biomarkers. On the other hand, Swin Transformer- Base achieved the best performance on several biomarkers (MI, HCC-108, HCC-18, HCC-96).
Classification accuracies tracked AUROC and generally fell in the 0.59-0.83 range depending on task difficulty, while Fi-scores were modest due to class imbalance and conservative thresholds used during testing. Detailed results with all metries are provided in Appendix A.55
Taken together, Tables and 2 highlight that diverse architectures, including the newly added Swin Transformer- Base, can learn discriminative representations from abdominal surface geometry to predict clinically meaningful biomarkers, and we expect that scaling to larger more specialized backbones will further improve performance.
6.1 ARCHITECTURAL FAMILY ANALYSIS
Within architectural families, observed that ResNet-18 often led on biomarkers (HCC-108, HCC- 111, HCC-18, and Mortality), whereas ResNet-34 performed the best on Calcium Score and Type 2 Diabetes. ResNet-50 (RadImageNet) underperformed ResNet-11/34 on most biomarkers despite greater capacity and domain specific pre-training. EfficientNet-BB BO was best on Age (regression) and good on other binary tasks (HCC-96, MI), showing strong accuracylefficiency trade-offs. DenseNet- 121 was consistently strong but rarely the best, the model ranked near the top on Calcium Score and Mortality. ViT-Small with DINOv2 pre-training showed competitive performance across all tasks, but never achieving the top result. This transformer architecture demonstrated robustness, but its self-attention mechanism could not surpass CNN basclines on this medical imaging benchmark, which may potentially be caused by the specific pre-training. On the other hand, Swin Transformer- Base achieved the best AUROC on several biomarkers (MI, HCC- HCC-18, HCC-96)
We believe that the task of predicting internal biomarkers from abdominal surface geometry fun- damentally differs from conventional medical imaging. As noted in Section 2.2. AbdCTBench re- quires learning "associations between geometry and physiology" from indirect predictive signal.
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CNNs' inductive bias for local feature extraction through convolutional operations appears partic- ularly well-suited for this task, as surface geometry patterns that correlate with internal biomarkers likely manifest as local spatial features (c.g., subtle curvature variations, adipose tissue distribution patterns) rather than global dependencies.
This aligns with established findings that CNNs excel capturing local spatial patterns through their cunsslaion-equivarre convolutional operations (Dosovitskiy et al.] 2021b), whereas vision trans- formers, while powerful for global context, may require more data architectural modifications to effectively capture fine-grained local features. Swin Transformer- Base (which incorporates hierar- chical local attention through shifted windows) achieving best performance on several biomarkers supports this interpretation, as that architecture balances local and global feature extraction.
6.2 DOMAIN SPECIALIZZTION IMPACT
ResNet-50 models initialized from RadImageNet did not outperform lighter architectures trained with standard initializations. For example, on Mortality prediction, the RadImageNet pre-trained ResNet-50 reached an AUROC of 0.810. lagging ResNet-18 (0.839) and EfficientNet-B0 BO (0.830). Similarly, on HCC tasks, ResNet-50 results (c.g.. 0.718 on HCC-108, 0.738 on HCC-96) trailed the best smaller models. While RadImageNet pre-training is lomain-specifer the difference between AbdCTBench and typical CT-scans, MRIs, or X-rays may be crucial: AbdCTBench captures the abdominal surface geometry derived from CT-scans rather than the raw CT imagery. Consequently, models with standard initializations consistently outperform the RadImageNet initialized ResNet- 50.
ViT-Small with DINOv2 pretraining, while competitive, also did not outperform the best CNN ar- chitectures. This indicates that both medical-domain pretraining in larger backbones and general vision transformer pretraining are not sufficient by themselves to overcome optimization and gener- alization benefits offered by smaller, regularized networks in this specific setting.
6.3 TIRRESIOLD-DEPEEDDEN METRICS
We report threshold-derived metrics at fixed operating points used during testing (often 0.8-0.9) in Appendix A.5 Under these settings, several biomarkers exhibit high recall but low precision (c.g.. HCC-18 with ResNet-18: recall 0.93 vs. precision 0.15), or conversely high specificity with mod- erate recall (e.g., Calcium Score with ResNet-34: specificity 0.758, recall 0.773). This underscores the importance of task-specific threshold selection: the same classifier can trade precision and recall substantially without changing AUROC. We reiterate that for each task and architecture, we select the F1 F1-optimal threshold from the validation set, and use the same threshold to report metrics from the test set. Complete threshold-dependen metrics with 95% CIs are reported in Appendix A.5
6.4 SUBGROUP ANALYSIS
To understand how model performance varies across patient demographics, we conducted subgroup analyses stratified by gender. For each biomarker, we performed the subgroup analysis by using the best-performing model (prior to the addition of Swin Transformer-Bann experiments) on the test set. These analyses reveal important heterogeneity in predictive performance that may inform clinical deployment strategies. For age prediction, models achieved substantially better performance in male patients (MAE 5.76, R² 0.81) compared female patients (MAE 6.63, R2 0.70), suggesting that abdominal surface geometry may be more predictive of age in males. This difference may reflect biological variations in body composition changes with age between genders. For binary classification tasks, gender differences varied by biomarker. Males showed superior performance on several cardiovascular and respiratory conditions: Calcium Score (AUROC 0.858 vs. 0.838), MI (AUROC 0.724 vs. 0.699), and HCC-111 (AUROC 0.802 vs. 0.740). In contrast, females demonstrated better performance on HCC-18 (AUROC 0.824 vs. 0.773) and Mortality prediction (AUROC 0.844 vs. 0.831).
These gender-stratified differences likely reflect genuine biological variation in how body compo- sition relates to health outcomes. Sex differences in fat distribution (gynoid android adiposity) and age-related changes (menopause effects) are well-documented (Lee et al., 2013) Lovejoy et al.] 2008). The improved age prediction in males may reflect more consistent age-related changes in

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male body composition, while the better diabetes complication detection in females aligns with known sex differences in diabetes presentation and complications (Wells) 2007 Regitz-Zagrosse 2012). Importantly, performance remains clinically meaningful across both groups, with no sub- group showing near-chance performance. These findings suggest that while sex-specific model training may yield improvements, unified model provides robust predictions across demographies.
Table 3: Gender-stratified performance metrics on the test set. Bootstrapped 95% CIs are shown in parentheses.
Biomarker
Best Model
Overall
Male (47.8 %6)
Female (52.2%)
Difference (Female Male)
Age (MAF)
EfficientNet-BO
6.223
5.76 (5.51-6.05)
6.63 (6.34-6.93)
+0.87
Calcium Score
ResNet-34
0.848
0.858 (0.835-0.879)
0.838 (0.812-0.861)
-0.020
MI
EfficientNet-BO
0.732
0.724 (0.691-0.756)
0.699 (0.661-0.736)
-0.025
Mortality
ResNet-18
0.839
0.831 (0.801-0.860)
0.844 (0980--0))77)
+0.013
T2D
34
0.742
0.740 (0.710-0.769)
0.743 (0.715-0.77) 0.777))
+0.003
HCC-108
ResNet-18
0.763
0.763 (0.730-0..) 0.794)
0.766 (0.735 0.795)
+0.003
HCC-111
ResNet-18
0.769
0.802 (0.749-0.4) 0,847)
0,740 (0.691-0,788)
-0,062
HCC-12
ResNet-34 34
0.591
0.623 (0.575-00 -06700
0.552 (0.503-0.603)
-0.071
HCC-18
ResNet-18 0.799
0.773 (0.728 0.814)
0.824 (0.789-08859)
+0.051
HCC-96
EfficientNet-B0
0.763
0.756 (0.714-0.794)
0.774 (0.731-0.815)
+0.018
Because chronological age is one of our main prediction targets in AbdCTBench, stratifying per- formance by age would conflate subgroup effects with task-defined signal. We therefore avoid age-based subgroup reporting and instead use gender, which is independent of the benchmark's prediction targets and has sufficient sample size for reliable analysis.
We performed additional follow-up experiments with Multi-Task Learning as detailed in Appendix A.6 Further, to demonstrate the effectiveness of learning representations from abdominal surface geometry, present additional analysis by using saudient-weighter Class Activation Mappings (Grad-CAM) (Selvaraju et al. 2017) in Appendix A.7
LIMITATIONS AND FUTURE DIRECTIONS
Our benchmarking focuses on single-biomarke predictors to isolate signal detectability and ar- chitectural effects. Extending to multi- target learning (shared encoders, task-specific heads) is promising direction to leverage inter-target correlations (we present preliminary experiment results in Appendíx [A.)): given the strength of smaller CNNs here, hypothesize shared lightweight backbones with calibrated thresholds could improve macro-level performance without sacrificing ef- ficiency. Further, due to computational constraints, we limited our benchmarking include smaller convolutional neural networks and transformer architectures. While ViT-Small showed competi- tive performance and Swin Transformer-Base performed the best on a few biomarkers, future work should explore larger vision transformers, different pretraining strategies (c.g.. medical-specific self- supervised learning), specialized architectures such as U-Net variants (Zhou al. [20 8: Chen et al.) 2021b Lu et al.) [2022; Vasa et al., (2024). and architectural modifications tailored to medical imag- ing- Additionally, incorporating calibration methods (temperature scaling, focal loss tuning) and uncertainty estimation may yield better decision thresholds.
From the perspective of real-world deployment of these models for low-cost, non-invasive car- diometablic risk assessment, cross-site validation is key next step, as AbdCTBench was collected from a single site. While we have standardized the dataset curation and benchmarking procedure, protocols to conduct CT-scans may vary slightly across sites, and thus may meaningfully change the surface geometry visible in the corresponding abdominal surface images. With the goal of capturing the most expansive dataset possible from the collection site, the absence of specific in- clusion/exclusion criteria (c.g. related to age, sex, race, pre-existing conditions, etc.) may have introduced implicit demographic biases in the dataset. Thus, multi-site evaluation is crucial for generalizability assessment.
Finally, validation of the models using surface geometry captured from consumer-grade devices would take us closer to the goal of widely accessible cardiometablic risk assessment.
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ACKNOWLEDGMENTS
SK is partially supported by NSF 2046795 and 2205329, IES R305C240046, ARPA-H, the MacArthur Foundation, Schmidt Sciences, Stanford HAI, RAISE Health, OpenAI, Microsoft, and Google.
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A APPENDIX
A. BIOMARKER DETAILS FOR FIGURE
This section provides complete biomarker details for the sample images shown in Figure)
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Table 4: Complete biomarker details for the sample images shown in Figure
Image
Gender
Male
Male
Female
Female
Age (years)
66
75
Calcium Score
Present
Absent
Present
Absent
MI
Absent
Absent
Present
Absent
Mortality
Absent
Absent
Present
Absent
T2D
Present
Absent
Present
Absent
HCC--108
Absent
Absent
Present
Present
HCC-111
Absent
Absent
Absent
Absent
HCC-12
Absent
Absent
Absent
Absent
HCC-18
Absent
Absent
Absent
Absent
HCC-96
Absent
Absent
Absent
Absent
A.2
DATASET RELEASE STATEMENT
The AbdCTBench dataset is released at lttpss:/sstale saass/aaarrraa.a.aaaeeeeeaaaaale 1olthab.../AbdCTBeeni. under Creative Commons BY 4.0 license. The release includes:
23,506 2D depth map projections (PNG format)
23,506 3D STL surface meshes
Complete DICOM-to-STL--O-PNN processing pipeline (Python code)
OSCAR biomarker extraction pipeline
Pre-trained model checkpoints for all 8 architectures (including Swin and multi task mod-
els)
Train/val/test splits and evaluation protocols
HIPAA-compliant de-identified labels: 87 comorbidities, diagnoses, 16 biomarkers
While multi-site CT datasets exist (e.g., Stanford Merlin), they lack the HCC and ICD-10 diagnosis codes required for benchmark tasks. Integrating imaging with structured clinical outcomes scale remains challenging. With the release of AbdCT'Bench and all associated elements, any insti- tution with CT DICOM series and corresponding HCC/CCD-10 codes reproduce the benchmark and validate results locally. Given current data availability, this is the most practical and scalable path forward for multi-site validation.
Further, no publicly available datasets currently contain paired consumer depth sensor captures and CT scans of abdominal regions, order quantify the fidelity gap. With the release of Abd- CTBench, researchers with consumer depth devices can apply our pipeline and quantify geometric fidelity gaps. Modern smartphone LiDDAR (~:5m depth resolution) should capture coarse body contour features that our models learn. We view AbdCTBench as establishing proof- proof-of-concept that surface geometry contains predictive biomarker information, with the open-source pipeline enabling community-driven real-world validation.
A.3 TECHNICAL DETAILS OF DICOM TO STL TO PNG CONVERSION
Once the DICOM image series are provided as input and loaded, the volume processing step applies optional shrinking to reduce volumes to maximum 256 voxels using shrink factors calculated per dimension (optionally applied when the shrink factor exceeds 3). Anisotropic smoothing uses sitk. CuraatureeAnssooopppffffufuoo with a time step of 0.03 to convert the data to sitkFloat32 format for further processing. Then, the surface extraction step generates 3D triangular meshes using VTK's contour filter, and the subsequent mesh processing applies refinement such as mesh cleaning, small object removal, and mesh smoothing. The processed mesh is then exported binary STL. file.
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The STL to 2D image conversion step uses square 384 384 pixel window, automatically centers the mesh at the origin, positions the camera using a distance factor 3.0, applies an 80% zoom factor, and includes fixed 10 rotation around the Z-axis. Following the application of 5000 mesh smoothing iterations, the output is a PNG image of size 384 x 384 pixels.
A.4 COMPLETE DATASET STATISTICS
Below we present a variety of dataset statistics, feature distributions, variable correlations, and pa- tient clustering visualizations.
Table 5: Complete CT-derived biomarker statistics for AbdCTBench dataset.
Biomarker
Count
Mean
Std Dev
Min
Max
BMD L1 High Sensitivity (HU)
22,083
148.4
44.3
-46.2
603.2
BMD Standard (HU)
22,119
179.1
54.4
-41.1
1,180.1
Calcium Scoring Abdominal Agalston
23,506
1,200.9
3,126.5
37,152.0
Kidney Median HU
23,279
90.0
58.8
5.0
298.9
Kidney Volume (cm
23,401
349.0
94.8
50.0
750.0
L3 SAT Area (cm²)
23,467
218.4
111.2
5.9
838.2
L3 TAT Arca (cm² 2)
23,467
365.2
177.0
8.5
1,136.5
L3 VAT (cm
23,467
146.9
104.6
647.7
L3 VAT Median (HU)
23,418
-89.0
10.5
-118.5
-30.0
L3 VAT/SAT Ratio
23,466
0.73
0.56
0.0
4.46
Liver Median HU
23,501
82.3
30.3
-24.7
233.2
Liver Volume (cm²)
23,498
1,578.4
432.8
269.0
4,973.0
L3 Muscle Area (em²)
23,469
147.5
37.4
29.2
315.3
L3 Muscle Mean HU
23,468
36.2
16.4
-47.5
89.8
Spleen Median HU
23,473
82.1
37.2
10.3
488.1
Spleen Volume (cm²)
23,242
223.9
127.1
50.0
4,323.0
HCC Code
Condition
Prevalence (%)
HCC-108
Vascular Disease
22.6
HCC-19
Diabetes Complications Breast, Prostate,
without
13.0
Diagnosis
Prevalence (%)
Fssential Hypertension
53.7
HCC-12
10.9
Type 2 Diabetes
44,6
Other Cancers Congestive Heart
Impaired Glucose Tolerance
38.0
HCC-85
9.9
Tobacco Use
26.8
Failure
Myocardial Infarction
23.1
HCC-48
Coagulation defects
9.8
Osteoporosis
14.7
HCC-18
Diabetes with
8.3
Heart Failure
11.4
Chronic tions
Complica-
Cardiovascular Disease (CVD)
15.5
Hypertensive
10.0
HCC-11
COPD
5.6
Chronic Kidney Discase (CKD)
HCC-40
Rheumatologic
6.1
Chronic Liver Disease
7.8
arthritis
HCC-23
Other Significant En- docrine Disorders
5.0
HCC-22
Morbid obesity
4.7
Table 6: Clinical diagnosis prevalence (left) and HCC comorbidity codes (right) in AbdCTBench.
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Figure 3: CT-derivedbiomarrer distributions across the AbdCTBench dataset.
Figure 4: Prevalence (a) diagnosis (ICD-10) codes, and (b) Hierarchical Condition Categories
(HCC) codes.
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HigH
101
High
W7.7
Figure 5: Correlations between (a) CT-derived biomarkers, and (b) HCC codes.
Not ane Cnters bosk PCA Stuce
Figure 6: (a) Longitudinal laboratory value distributions, and (b) patient clustering patterns. The clusters were obtained via k-means clustering (with k 4) by using the first 22 Principal Components (which explained 80% of the variance in the data from all the numeric features).
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A.5 DETAILED RESULTS BY BIOMARKER
This section provides comprehensive results for each biomarker across all architectures, including all metries with 95% confidence intervals computed via patient-level bootstrapping.
Table 7: Complete results for Age prediction across all architectures.
Architecture
MAE
MSE
Naive Baseline
13.16
265.11
-0.0001
(12.79, 13.57)
(252.99, 278.70)
(-0.0030, 0,0000)
DenseNet-121 121
6.769
74.391
0.719
(6.551, 6.994)
(69.636, 79.591)
(0.696, 0.741)
EfficientNet-BO
6,223
64.447
0,757
(6.016, 6.422)
(60. 141, 166)
(0.735, 0.776)
ResNet-18
6.472
67.985
0.744
(6.264, 6.678)
(63.650, 73.052)
(0.722, 0.763)
ResNet-34
6,486
68.853
0.740
(6.284, 6.692)
(64.397, 73.698)
(0.718, 0.761)
ResNet-50
6.341
65.604
0,753
(6.154, 6,532)
(61.517, 70.203)
(0.733, 0,770)
ViT-Small
6.465
70.542
0.734
(DINOv2)
(6.260, 6,684)
(65.9 (65.913, 75.829)
(0.710, 0.755)
Swin
6.540
70.80
0.733
Transformer-Base
(6.338, 6.758)
(66.43, 75.85)
(0.710, 0.754)
Table 8: Complete results for Calcium Score prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.786
(0.770, 0,803)
121
0.847
0.497
0.746
0.795
0.597
0.784
0.8
(0.829, 0.863)
(0.460, 0.531)
(0.707, 0.782)
(0.776, 0.813)
(0.563, 0.626)
(0.767, 0,800)
EfficientNet-BO
0.847
0.481
0.765
0.775
0.591
0.773
0.8
(0.829, 0.864)
(0.446, 0,515)
(0.728, 0.803)
(0.757, 0.794)
(0.557, 0.621)
(0.757, 0,790)
ResNet-18
0.843
0.442
0,817
0,719
0,573
0,740
0.7
(0.825, 0.859)
(0.407, 0.472)
(0.782, 0.849)
(0.698, 0.738)
(0.540, 0.601)
(0.721, 0.756)
ResNet-34
0.848
0.465
0.773
0.758
0.581
0.761
0.8
(0.831, 0.864)
(0.433, 0.498)
(0.737, 0.807)
(0.738, 0.778)
(0.549, 0.609)
(0.744, 0.779)
ResNet-50
0.833
0.411
0.837
0,673
0,551
0,708
0.5
(0.815, 0.849)
(0.379, 0.439)
(0.801, 0.868)
(0.651, 0.695)
(0.518, 0.579)
(0.688, 0.727)
ViT-Small
0.829
0.426
0.819
0.700
0.561
0.725
0.8
(DINOv2)
(0.809, 0.846)
(0.394, 0.455)
(0.784, 0.852)
(0.678, 0.719)
(0.529, 0.589)
(0.706, 0.742)
Swin
0.845
0.474
0.769
0.767
0.586
0.768
0.9
Transformer-
(0.828, 0.862)
(0.438, 0.505)
(0.729, 0.805)
(0.748, 0.786)
(0.554, 0.615)
(0.750, 0.784)
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Table 9: Complete results for HCC-108 prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
Fl
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.763
(0.745, 0.780)
DenseNet-121
0.740
0.384
0.709
0.647
0.498
0.662
0.8
(0.720, 0.760)
(0.354, 0,414)
(0.673, 0.747)
(0.626, 0.669)
(0.468, 0.526)
(0.643, 0.681)
EfficientNet-BO
0.753
0.376
0.775
0.601
0.507
0.643
0.8
(0.732, 0.775)
(0.349, 0,405)
(0.741, 0,807)
(0.579, 0.624)
(0.477, 0.536)
(0.623, 0.663)
ResNet-18
0.763
0.375
0.791
0,590
0.508
0.638
0.7
(0.742, 0.784)
(0.347, 0.404)
(0.759, 0.822)
(0.567, 0.612)
(0.480, 0.539)
(0.620, 0.657)
ResNet-34
0.749
0.362
0.804
0.561
0.499
0.619
0.8
(0.729, 0.769)
(0.336, 0.390)
(0.770, 0.834)
(0.539, 0.584)
(0.470, 0.528)
(0.599, 0.638)
ResNet-50
0.718
0.368
0.737
0.607
0.491
0.638
0.2
(0.698, 0.739)
(0.340, 0.396)
(0.700, 0.772)
(0.584, 0.629)
(0.462, 0,519)
(0.620, 0,655)
ViT-Small
0.743
0.375
0.730
0.623
0.496
0.649
0.8
(DINOv2)
(0.723, 0.764)
(0.348, 0.403)
(0.696, 0.766)
(0.603, 0.645)
(0.467, 0.524)
(0.631, 0.667)
Swin
0.768
0.378
0.800
0.592
0.514
0.641
0.8
Transformer-Bas -Bas
(0.749, 0.788)
(0.352, 0.407)
(0.766, 0.830)
(0.569, 0.614) (0.486, 0.543)
(0.623, 0.661)
Table 10: Complete results for HCC-111 prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.929
(0.919, 0.939)
DenseNet-122 121
0.716
0.122
0.790
0.570
0.212
0.585
0.9
(0.681, 0.749)
(0.103, 0.142)
(0.731, 0.844)
(0.550, 0,591)
(0.181, 0,242)
(0.567, 0.605)
EfficientNel-BO
0.747
0.131
0.717
0.638
0.221
0.643
0.9
(0.708, 0.782)
(0.111.0.152)
(0.643, 0.784)
(0.617, 0.658)
(0.190, 0.254)
(0.621, 0.662)
ResNet-18
0.769
0.121
0.843
0.533
0.211
0.555
0.9
(0.734, 0.802)
(0.103, 0.140)
(0.788, 0.895)
(0.513, 0.554)
(0.182, 0,240)
(0.535, 0.575)
ResNet-34
0.766
0.114
0.886
0.479
0,203
0,508
0.9
(0.730, 0.799)
(0.097, 0.133)
(0.829, 0.933)
(0.459, 0,500)
(0.175, 0.231)
(0.488, 0.528)
ResNet-50
0.739
0.154
0.416
0.826
0.224
0.797
0.2
(0.704, 0.774)
(0.123, 0.187)
(0.343, 0.497)
(0.811, 0.841)
10.182, 0.269)
(0.780, 0.813)
ViT-Small
0.760
0.117
0.898
0.483
0,206
0.512
0.9
(DINOv2)
(0.727, 0,793)
(0.100, 0.14
(0.853, 0.939)
(0.463, 0,504)
(0. 17.179, 0,233)
(0.492, 0,532)
Swin
0.765
0.150
0.753
0.676
0.251
0.682
0.9
Transformer-Ban
(0.732, 0.796)
(0.128, 0.17
(0.687, 0.817)
(0.656, 0.696)
(0.217, 0.284)
(0.663, 0.700)
Table 11: Complete results for HCC-12 prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.884
(0.871, 0.897)
DenseNet-121 121
0.587
0.136
0.585
0.513
0.220
0.521
0.7
(0.551, 0.622)
(0.117, 0.144)
(0.526, 0.642)
(0.493,
0.534)
(0.192, 0.247)
(0.502, 0.540)
EfficientNet-BO
0.586
0.162
0.456
0.691
0.239
0.663
0.3
(0.550, 0.623)
(0.136, 0,189)
(0.394, 0,512)
(0.670, 0,711)
(0.205, 0,273)
(0.643, 0,683)
ResNet-18
0,580
0.134
0.713
0.397
0.226
0,434
0.9
(0.544, 0.614)
(0.118, 0.153)
(0.659, 0.766)
(0.377, 0.418)
(0.201, 0.253)
(0.415, 0.455)
ResNet-34
0.591
0.148
0.621
0.533
0.240
0.543
0.9
(0.557, 0.624)
(0.129, 0.0)
(0.566, 0.674)
(0.511, 0.554)
(0.211, 0.269)
(0.524, 0.563)
ResNet-50
0.571
0.150
0.434
0.678
0,223
0.650
0.9
(0.536, 0.607)
(0.128, 0.176)
(0.380, 0.495)
(0.658, 0.697)
(0.192, 0.257)
(0.630, 0.669)
VIT-Small
0.576
0.140
0.449
0.640
0.214
0.618
0.9
(DINOv2)
(0.542, 0.610)
(0.119, 0.163)
(0.390, 0.508)
(0.620, 0.661)
(0.184, 0.245)
(0.599, 0.636)
Swin
0,580
0.133
0.728
0,378
0,225
0.418
0.8
Transformer-Base
(0.545, 0.616)
(0. 0.150)
(0.674, 0.777)
(0.358, 0.399)
(0.200, 0.250)
(0.398, 0.440)
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Table 12: Complete results for HCC-18 prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
Fl
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.908
(0.896, 0.920)
DenseNet-121
0.766
0.236
0.454
0.852
0,311
0.815
0.8
(0.735, 0.797)
(0.197, 0,281)
(0.388, 0.528)
(0.836, 0.866)
(0.266, 0.362)
(0.800, 0.831)
EfficientNet-BO
0.789
0.203
0.764
0.697
0.321
0.703
0.9
(0.760, 0.816)
(0.177, 0.232)
(0.706, 0.819)
(0.677, 0.716)
(0.285, 0.359)
(0.685, 0.721)
ResNet-18
0.799
0.145
0.926
0.446
0,250
0.490
0.9
(0.770, 0.828)
(0.127, 0.164)
(0.886, 0.960)
(0.426, 0.467)
(0.222, 0.279)
(0.472, 0,510)
ResNet-34
0.775
0.167
0.857
0.569
0.280
0.595
0.9
(0.746, 0.805)
(0.146, 0.190)
(0.808, 0.904)
(0.547, 0.589)
(0.249, 0.311)
(0.575, 0.613)
ResNet-50
0.782
0,218
0.648
0.765
0.326
0.754
0.9
(0.754, 0.812)
(0.188, 0.250)
(0.584, 0.715)
(0.748, 0.782)
(0.286, 0.369)
(0.737, 0,771)
ViT-Small
0.785
0.181
0.792
0.637
0.295
0,652
0.9
(DINOv2)
(0.755, 0.817)
(0.157, 0.208)
(0.738, 0.849)
(0.617, 0.658)
(0.261, 0.331)
(0.633, 0.671)
Swin
0.801
0.184
0.829
0.627
0,301
0.646
0.9
Transformer-Bas Basc
(0.776, 0.828)
(0.161, 0.210)
(0.779, 0.878)
(0.608, 0.646) (0.268, 0.335)
(0.627, 0.663)
Table 13: Complete results for HCC-96 prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.914
(0.901, 0.925)
DenseNet-122 121
0.757
0.156
0.818
0.581
0.262
0.601
0.9
(0.723, 0,787)
(0.134, 0,178)
(0.763, 0.866)
(0.560, 0.602)
(0.229, 0,294)
(0.582, 0.621)
EfficientNel-BO
0.763
0.152
0.818
0,569
0.257
0,591
0.9
(0.732, 0.790)
(0.131, 0.174)
(0.764, 0.866)
(0.548, 0.590)
(0.226, 0.288)
(0.571, 0.611)
ResNet-18
0.760
0.162
0.773
0.621
0.268
0.634
0.9
(0.727, 0.791)
(0.138, 0.186)
(0.715, 0.829)
(0.600, 0.641)
(0.233, 0,301)
(0.615, 0.653)
ResNet-34
0.728
0.177
0,453
0,801
0,255
0,771
0.8
(0.694, 0.761)
(0.144, 0.211)
(0.386, 0.519)
(0.785, 0.817)
(0.212, 0,297)
(0.755, 0.787)
ResNet-50
0.738
0.198
0.522
0,800
0.287
0.776
0.8
(0.705, 0.769)
(0.165, 0.232)
(0.448, 0.589)
(0.784, 0.817)
(0.244, 0.329)
(0.760, 0.792)
ViT-Small
0.757
0.162
0.773
0.621
0,268
0.635
0.9
(DINOv2)
(0.721, 0,788)
(0.139, 0,186)
(0.711, 0.828)
(0.601, 0,643)
(0.234, 0,302)
(0.615, 0,654)
Swin
0.770
0.178
0.699
0.695
0.284
0.695
0.9
Transformer-Bar
(0.739, 0.798)
(0.152, 0.206)
(0.632, 0.761)
(0.676, 0.714)
(0.246, 0.321)
(0.677, 0.713)
Table 14: Complete results for Myocardial Infarction (MI) prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.765
(0.748, 0.783)
DenseNet-121
0.730
0.377
0.674
0.657
0.483
0.661
0.8
(0.703, 0.752)
(0.346, 0.408)
(0.633, 0.713)
(0.636. 0.680)
(0.451, 0.514)
(0.642, 0.681)
EfficientNet-BO
0.732
0.399
0.687
0.683
0.505
0.684
0.9
(0.708, 0.754)
(0.367, 0.432)
(0.647, 0,722)
(0.663, 0,704)
(0.472, 0,536)
(0.665, 0,702)
ResNet-18
0,729
0.374
0,669
0,656
0,480
0.659
0.8
(0.705, 0.752)
(0.344, 0.405)
(0.631, 0.707)
(0.634, 0.678)
(0.449, 0.510)
(0.640, 0.678)
ResNet-34
0.731
0.375
0.681
0.651
0.483
0.658
0.8
(0.706, 0.753)
(0.346, 0.40S)
(0.640, 0.719)
(0.630, 0.673)
(0.452, 0.513)
(0.639, 0.678)
ResNet-50
0.716
0,346
0.717
0,583
0.467
0.615
0.8
(0.693, 0.739)
(0.316, 0.373)
(0.679, 0.755)
(0.561, 0.606)
(0.435, 0.496)
(0.595, 0,634)
VIT-Small
0.732
0.347
0.823
0.525
0.488
0.595
0.7
(DINOv2)
(0.707, 0.754)
(0.320, 0.374)
(0.790, 0.852)
(0.501, 0.547)
(0.457, 0.515)
(0.574, 0.615)
Swin
0.742
0.368
0,748
0,606
0.493
0.639
0.8
Transformer-Base
(0.718, 0.763)
(0.338, 0.394)
(0.710, 0.784)
(0.583, 0.628)
(0.461, 0.521)
(0.620, 0.658)
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Published as a conference paper at ICLR 2026
Table 15: Complete results for Mortality prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
Fl
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.886
(0.874, 0.900)
DenseNet-122
0.823
0.281
0.727
0.761
0.405
0.757
0.9
(0.800, 0.845)
(0.247, 0.311)
(0.675, 0.779)
(0.744, 0.779)
(0.364, 0.442)
(0.741, 0.774)
EfficientNet-BO
0.830
0.318
0,633
0.826
0,423
0.804
0.9
(0.805, 0.852)
(0.277, 0.355)
(0.574, 0.687)
(0.810, 0.841)
(0.377, 0.463)
(0.787, 0.819)
ResNet-18
0.839
0.289
0.749
0.764
0.418
0.763
0.9
(0.816, 0.861)
(0.256, 0.321)
(0.694, 0,800)
(0.748, 0.782)
(0.379, 0.455)
(0.746, 0.779)
ResNet-34
0.825
0.359
0.581
0.867
0,444
0.835
0.8
(0.799, 0.848)
(0.310, 0.405)
(0.516, 0.637)
(0.853, 0.881)
(0.391, 0,489)
(0.820, 0.849)
ResNet-50
0.810
0.220
0.850
0.613
0.349
0.640
0.9
(0.784, 0.834)
(0.194, 0.242)
(0.809, 0.891)
(0.593, 0.632)
(0.315, 0.378)
(0.620, 0.658)
ViT-Small
0.811
0,255
0.745
0.720
0.379
0.723
0.9
(DINOv2)
(0.785, 0.836)
(0.223, 0.285)
(0.690, 0.796)
(0.701, 0.739)
(0.338, 0,417)
(0.705, 0,742)
Swin
0.828
0,280
0.749
0.753
0.407
0.752
0.9
Transformer Base
(0.803, 0.851)
(0.245, 0.312)
(0.696, 0.799)
(0.735, 0.772)
(0.367, 0.445)
(0.736, 0.770)
Table 16: Complete results for Type-2 Diabetes prediction across all architectures.
Architecture
AUROC
Precision
Recall
Specificity
F1
Accuracy
Threshold
Naive Baseline
0.5
0.0
0.0
1.0
0.0
0.551
(0.531, 0.571)
DenseNet- 121
0.728
0.533
0.909
0.351
0.672
0.601
0.4
(0.709, 0.750)
(0.509, 0.555)
(0.892, 0.926)
(0.323, 0.376)
(0.650, 0.690)
(0.580, 0.621)
EfficientNe1-BO
0.740
0.532
0.915
0,344
0.673
0.600
0.3
(0.720, 0.761)
(0.508, 0.553)
(0.897, 0.931)
(0.318, 0.368)
(0.652, 0.691)
(0.580, 0.619)
ResNet-18
0.735
0.551
0.888
0.409
0.680
0.624
0.4
(0.714, 0.756)
(0.526, 0.573)
(0.869, 0.906)
(0.381, 0.434)
(0.659, 0.699)
(0.603, 0.643)
ResNet-34
0.742
0.538
0.913
0,361
0.677
0.609
0.4
(0.722, 0,762)
(0.514, 0,560)
(0.894, 0.929)
(0.335, 0,387)
(0.656, 0.695)
(0.589, 0.628)
ResNet-50
0.733
0.550
0.881
0.412
0.677
0.623
0.4
(0.714, 0.753)
(0.526, 0.574)
(0.862, 0.899)
(0.386, 0.440)
(0.656, 0.696)
(0.603, 0.641)
ViT-Small
0.735
0.540
0.895
0.379
0.674
0.611
0.4
(DINOv2)
(0.714, 0,755)
(0.517, 0.562)
(0.875, 0,913)
(0.354, 0.405)
(0.652, 0.692)
(0.591, 0,630)
Swin
0.740
0.533
0.917
0.347
0.674
0.603
0.2
Transformer Base
(0.720, 0.759)
(0.510, 0.556)
(0.900, 0.933)
(0.321, 0.373)
(0.654, 0.692)
(0.583, 0.622)
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Published as a conference paper at ICLR 2026
A.6 MULTI-TASK LEARNING
In follow-up experiment, we implemented a multi-task learning framework covering all 10 bench- marked biomarkers, by training multi-task ResNet-18, ResNet-34, and ResNet-50 (RadImageNet) models. Each of these had a shared backbone, followed by task heads. Further, we kept the standardized tingle-target training protocol (learning rates, optimizers, augmentations, schedulers) wherever applicable, so the comparison isolates the multi-task objective rather than tuning differ- ences. Each mini-batch optimized the sum of per-task losses, cross-entropy for the classification biomarkers and MSE for age regression. We incorporated GradNorm (Chen et al. 2018) to balance gradient magnitudes so that casier tasks cannot dominate optimization. Model selection used the median AUROC across the binary biomarkers on the validation split, ensuring that gains arise from broad improvements instead of a single outlier task.
This multi-task extension shows that AbdCTBench's standardized setup supports joint training. However, the results we obtained indicate that multi-task learning did not substantially improve, rather degraded performance, relative to single-task modeling:
Table 17: Multi-task learning results for non-HCC biomarkers by architecture on the test set. AU-
ROC is reported for the binary classification tasks. MAE is reported for Age prediction (regression task). Bootstrapped 95% CIs are shown in parentheses.
Architecture
Age (MAF)
Calcium Score
MI
Mortality
T2D
ResNet-18
14.529
0.625
0.592
0.615
0.584
(14.082-15.013)
(0.597-0.652)
(0.568-0.619)
(0.578-0.649)
(0.561-0.66))
ResNet-34
21,834
0.522
0,546
0,474
0.592
(21.275-22.446))
(0.492-0.552)
(0.519-0.574)
(0.435-0.514)
(0.569-0.614)
ResNet-50
72.645
0.612
0.546
0.632
0.524
(RadImageNet)
(21.880-133.051)
(0.583-0.640)
(0.517-0.574)
(0.596-0.665)
(0.501-0.547)
Table 18: Multi-task learning results for HCC code biomarkers by architecture on the test set. All biomarkers report AUROC. Bootstrapped 95% CIs are shown in parentheses.
Architecture
HCC108
HCC111
HCC12
HCC18
HCC96
ResNet-18
0.603
0,508
0.527
0.590
0.611
(0.577-0.66 0.631)
1.464-0.5511
(0.494-0.564)
(0.551-0.62))
00.567-0.553
ResNet-34
0.537
0.507
0.508
0.655
0.530
(0.510-0.566)
(0.463-0.550)
(0.472-0.544)
(0.614-0.692)
(0.486-0.576)
ResNet-50
0.552
0.628
0.492
0.570
0.574
(RadImageNet)
(0.525-0.577)
(0.583-0.673)
(0.456-0.532)
(0.527-0.614)
(0.528-0.614)
The results above indicate that there may be negative transfer between the full set of 10 biomarkers. Further, the standardized training protocol for single-target modeling may not be amenable to the multi-task training problem, and will need to be investigated further. Multi-task learning with a smaller subset of biomarkers and hyper-parameters tuned specifically for that set of biomarkers may yield substantially better results, and AbdCTBench is training-ready for extensive multi-task learning to be undertaken as future work.
A.7 EFFECTIVE LEARNING OF REPRESENTATIONS FROM ABDOMINAL SURFACE GEOMETRY
From the performance metrics reported above, we do not see drastic differences between the ar- chitectures considered. This demonstrates the viability of effective representation learning from abdominal surface geometry for clinically relevant biomarker prediction. The effectiveness is ob- served across all architectures and biomarkers, except HCC-12 (Breast, Prostate, and other Cancers), whereby external surface geometry may not be predictive of the comorbidity from a clinical per- spective either. To demonstrate this further, we apply Gradient- Gradient-Weighted Class Activation Mapping (Selvaraju al. 2017) to the input images to visualize the representations.
In specific, we load ResNet-18 (one of the best-performing models on HCC-18, Diabetes with Chronic Complications). and apply Grad-CAM on the last convolution layer visualize the fea-
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Published as a conference paper at ICLR 2026
tures learned from the surface geometry images. We collect small random sample of size 100 from the test set, and select compelling examples to demonstrate the effectiveness the learned representations as hypothesized. The heatmaps identify high attention regions from the surface ge- ometry for the model to make predictions. The Fl Fo-pttimal threshold of 0.9 was applied for the binary classification.
Figure 7: Grad-CAM visualizations showing learned representations from abdominal surface geom- etry. The heatmaps highlight regions of interest that the ResNet-18 model focuses on for HCC-18 (Diabetes with Chronic Complications) prediction.
While Grad-CAM is popular interpretability method, it has been shown to be unreliable (Kinder- mans et al. 2019). We provide these visualizations as peoothesis-gnnrrttinn and these are not used to support any core claims. All of our main conclusions rely quantitative performance metries; the paper does not draw any causal or mechanistic inferences from A...
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Published as a conference paper at ICLR 2026
A.8 LLM USAGE DECLARATION
We declare that we used LLMss for the following tasks:
Literature review, finding relevant works, and understanding the state-of-the-art. All re- trieved information was manually verified and validated.
Improving the writing of the manuscript. All writing changes were manually verified and validated.
Code generation and debugging. All code changes were manually reviewed, tested, and validated.
25


Reviews:
[{"review_id": "3RrxGC6I1M", "reviewer": "Reviewer_yCJD", "summary": "This paper presents the AbdCTBench, a large-scale dataset of 23,506 CT-derived abdominal surface mesh images from 18,719 patients. The AbdCTBench contains 16 biomarkers paired with 31 diagnosis codes and 87 comorbidity labels. The authors established benchmarks on age prediction, mortality prediction, diabetes detection, etc., using six computer vision architectures.", "questions": "Is the dataset de-identified?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The constructed dataset is a large-scale CT image dataset. It contains 23,506 CT-derived abdominal surface mesh images from 18,719 patients. The images belong to 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers.\n\nThe authors conducted experiments using CV architectures ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, and computed the benchmarks for non-HCC biomarkers and HCC code biomarkers.", "weaknesses": "The AbdCTBench is not yet released/available.\n\nThe model architectures are small. It's better to test the ViT-base or larger models to evaluate their performance. \n\nIt's better to provide the internal body composition biomarkers for the 4 images shown in Figure 1.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WjI8WXCRqT", "reviewer": "Reviewer_8PVq", "summary": "This paper addresses ​​accessibility barriers in body composition analysis via CT and MRI​​ (e.g., radiation exposure, high costs) by introducing ​​AbdCTBench​​, a large-scale dataset containing 23,506 CT-derived abdominal surface mesh images paired with 87 comorbidity labels and 16 CT-derived biomarkers. The core method involves using ​​2D mesh projections​​ and multiple computer vision architectures (e.g., ResNet, EfficientNet, ViT) to learn representations from external surface geometry for predicting internal biomarkers. Key results show clinically relevant accuracy: age prediction (MAE 6.22 years), mortality prediction (AUROC 0.839), and diabetes detection (AUROC 0.799). The primary contribution is the ​​first and largest publicly available dataset bridging external body geometry with internal clinical measurements​​, enabling benchmarks for accessible medical AI. Experiments reveal that smaller architectures (e.g., ResNet-18) often match or surpass larger models, while medical-domain pretraining (e.g., RadImageNet) does not yield superior performance.", "questions": "Why did ViT-Small with DINOv2 pretraining not outperform CNNs? Is this due to local feature importance in surface geometry? Could larger transformers or medical-specific pretraining help?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- ​​Originality:​​ AbdCTBench is the first public dataset to systematically connect abdominal surface geometry with internal biomarkers at scale (23,506 samples), pioneering non-invasive health screening.\n- Quality:​​ Rigorous experimental design covers 6 architectures and 10 biomarker tasks, with standardized protocols (e.g., inverse frequency weighting for class imbalance) ensuring reproducibility. Grad-CAM visualizations (Figure 3) effectively illustrate learned representations.\n- Clarity:​​ Training details (e.g., hyperparameters, loss functions) are comprehensively documented, and appendices provide full statistics (Table 3-4), facilitating replication.", "weaknesses": "- Generalizability Concerns:​​ Data is from a single site, risking demographic biases (e.g., age/race distribution uncontrolled); multi-site validation is needed.\n- Limited Architecture Exploration:​​ Only standard CNNs/transformers are tested, omitting medical-specific models (e.g., U-Net variants) that might capture better representations. ViT-Small's competitive but suboptimal performance.\n\n- Uneven Task Performance:​​ HCC 12 prediction (AUROC ~0.59) is near-random, but no deep analysis explains why surface geometry fails here.\n\n- ​​No Multi-Task Learning:​​ The single-target framework misses biomarker correlations; multi-task learning (as noted in Section 7) could improve efficiency but is unexplored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "S9VJWAoe1W", "reviewer": "Reviewer_hrp9", "summary": "The paper proposes a large dataset consisting of surface projection images and associated biomarkers (derived from CT) and diagnosis labels. This is motivated by the opportunity to validate models that can predict the associated biomarkers and diagnosis labels using consumer grade 3D surface extraction devices. Additionally, the paper benchmarks representative architectures for the prediction tasks (classification and regression) including pretrained models on convolutional and transformer-based ones.", "questions": "Please refer to the input representation and disaggregated reporting bullet points in the section above.  \n\nIn addition to disaggregated reporting, Could you also report confidence interval of the reported metrics? Also, please clarify the reported performance with respect to clinical utility (readiness) and gap with respect to clinical requirements (for e.g. how small the Calcium Score should error be to be clinically usable in a specific diagnosis?). \n\nUse of heatmaps (Grad-CAM) to justify effectiveness of learnt representation has been shown to be problematic – useful for model debugging perhaps but controversial as a tool for pixel importance / visual explanations [1]. \n\n1. Kindermans, Pieter-Jan, et al. \"The (un) reliability of saliency methods.\" Explainable AI: Interpreting, explaining and visualizing deep learning. Cham: Springer International Publishing, 2019. 267-280. \n\n \n\nCertain sentences may require citations. E.g.  \n- While early implementations struggle with complex torso geometries,... \n- This limitation has led to the adoption of advanced imaging biomarkers ,... (Need citation associating tissue composition with CT) \n \nTypos \nSpacing typos: advancesspanning, modelsgeneralize,", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The effort to get approval, collect, preprocess and benchmark such a large dataset with a rich set of associated labels and biomarkers is commendable. The paper is mostly well written with data acquisition and preprocessing steps well explained. The benchmarking of models on the proposed datasets does cover representative architectures, including pretrained models.", "weaknesses": "*Input Representation*: Why surface projection images? Why not train on 3D surface meshes themselves to predict biomarkers and diagnostic labels? I.e. The paper is motivated by the possibility to use consumer grade devices such as LiDAR-enabled phones which can generate 3D surface meshes but the proposed dataset and model training use 2D projections which considerably reduces the available information. No rationale or experimental validation is given for this decision.  \n\n*Disaggregated reporting*: It is unclear from the reported aggregated metrics about the performance on clinically important subgroups. Without disaggregated reporting and subgroup analysis, the benchmarking may be incomplete with potential for underdiagnosis bias, subgroup disparities and bias and fairness issues to certain subgroups which may also highlight if such a mapping from surface projection to 3D-based biomarkers and diagnostic labels even make sense. Comparison with naive baselines such as just predicting average quantities will also be useful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9uX4gPj0b3", "reviewer": "Reviewer_CAtG", "summary": "The paper introduces AbdCTBench, a large-scale dataset comprising 23,506 CT-derived 2D abdominal surface meshes from 18,719 patients, paired with 16 quantitative CT biomarkers, 87 comorbidity labels, and 31 diagnosis codes. The core hypothesis is that external body surface geometry, capturable via consumer-grade depth sensors, can serve as a non-invasive proxy for internal tissue composition and clinical risk stratification. The authors benchmark six vision architectures (including CNNs, ViT, and medical-pretrained models) on 10 biomarker prediction tasks, demonstrating that even lightweight models can achieve clinically meaningful performance (e.g., mortality prediction AUROC = 0.839, age MAE = 6.22 years). The dataset and baselines are slated for public release to foster research in accessible, radiation-free health screening.", "questions": "Please address the aforementioned weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The work directly addresses a critical gap in preventive medicine: democratizing access to body composition biomarkers without CT/MRI. By leveraging surface geometry, a modality compatible with smartphones and LiDAR, the paper aligns with real-world trends in digital health and point-of-care screening.\n2. With 23K+ samples and 104 clinical variables, AbdCTBench is the largest publicly available dataset linking external surface geometry to internal CT-derived biomarkers. The inclusion of longitudinal lab values, HCC codes, and quantitative tissue metrics enables diverse downstream tasks.\n3. The authors implement a standardized training protocol (consistent optimizer, augmentation, class balancing, threshold tuning) across architectures, ensuring fair comparison. Results include bootstrapped confidence intervals, enhancing statistical reliability.\n4. Models achieve clinically actionable accuracy on key tasks (mortality, vascular disease, diabetes complications), supporting the feasibility of surface-based screening.", "weaknesses": "1. All CT scans originate from one private healthcare provider, raising concerns about demographic, geographic, and protocol-related biases. The authors acknowledge the lack of explicit inclusion criteria, which may limit generalizability.\n2. Surface meshes are derived from CT scans, not captured by real consumer devices (e.g., iPhone LiDAR). The fidelity gap between clinical CT-derived surfaces and real-world depth scans remains unquantified, casting doubt on real-world deployability.\n3. The benchmark excludes modern medical vision architectures (e.g., UNet variants, Swin Transformers, Mamba-based models), which could better exploit inter-biomarker correlations.\n4. From a technical and methodological standpoint, the paper's contribution lies primarily in dataset curation and empirical benchmarking using standard vision models, without novel architectures, training paradigms, or representation learning innovations. As such, it would be better suited for a dataset-and-benchmark track or a more clinically oriented venue focused on medical imaging and health informatics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)