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Open problems in Image segmentation

Lack of benchmarks for agentic evidence-seeking in medical imaging1 paper raises it · 1 addresses it
Current benchmarks assess reasoning over preselected evidence rather than the ability to autonomously seek and integrate longitudinal imaging and clinical records.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Scaling parameter-efficient fine-tuning for large medical models1 paper raises it · 1 addresses it
Neural data is costly and limited, making full fine-tuning or training from scratch impractical for large models in medical tasks like brain encoding.
- Scaling Parameter-Efficient Fine-Tuning for Brain Encodingraises · addresses
Generating coherent gigapixel whole-slide images1 paper raises it · 1 addresses it
Existing histopathology generative models operate at patch scale, while clinical supervision and diagnoses operate at whole-slide level, creating a mismatch.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Anatomically unsupported detail in generative super-resolution for CT1 paper raises it · 1 addresses it
Generative super-resolution can produce visually plausible but anatomically incorrect vascular structures in CT images.
- VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolutionraises · addresses
Frozen models unable to learn from deployment experience1 paper raises it · 1 addresses it
Deployed medical AI models are typically frozen, preventing them from adapting to new clinical evidence or updated guidelines.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

image segmentation · volumetric · medical image395 papers54 this week
clinical reasoning · question answering · language models202 papers35 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers19 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers13 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.AIOct 5
Adaptive Gait Biofeedback With Participant-Held-Out Modeling and Participant-Specific Updating in Chronic Ankle Instability
Jaeyoon Kim, Veronika Lebisova, Jeniya Sultana, Jaeyoung Cho, Jaeho Jang
Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human response. We evaluated a temporal convolutional classifier on protocol-defined, angle-derived GOOD/BAD gait-cycle labels using participant-held-out leave-one-subject-out (LOSO) cross-validation in 20 participants. Seven participants in the adaptive-intervention group completed nine sessions over three weeks, with one motion-capture recording analyzed per session. Models updated after failed sessions were compared offline with their parent models on the same-session validation subset used for candidate selection and the first subsequent adaptive-session recording. Frontal-plane ankle angle was compared between the adaptive group and 10 sequentially enrolled controls at Baseline, Post, and 7-day Retention. Across 20 held-out folds, mean fold-level area under the receiver operating characteristic curve (AUROC) was 0.948, sensitivity for angle-threshold-exceeding BAD cycles was 0.941, and specificity for angle-threshold-meeting GOOD cycles was 0.366. Mean BAD-class F1 was higher in candidate models by 0.187 on the same-session subset and…
PDF arXiv
arXiv cs.LGOct 5
Benchmarking Label-Revealed Online Updates for EEG BCI Decoding
Bogdan Kozyrskiy, Artem Grachev, Abraham I. Camelo Guerrero
Electroencephalography (EEG) signals drift over time, which can cause static brain-computer interface (BCI) models to degrade in practice. We present a benchmark for online adaptation and compare two widely used pipeline families, Common Spatial Patterns (CSP) and Riemannian covariance-based methods, under time-ordered prequential (test-then-train) evaluation. We examine (i) which pipelines benefit most from label-revealed updates, (ii) whether controlled forgetting of older data improves robustness, and (iii) how a minimal-calibration cold start compares with starting from a pretrained model. Across four datasets (three motor-imagery datasets and one movement-decoding dataset), label-revealed online updates improve 13 of 14 model/dataset pairs on the two largest streams, with relative accuracy gains of up to about 18% over a frozen model. A Shapley-based data-valuation analysis over temporal blocks assigns the largest mean value to the most recent block in each of the three analyzed datasets, while older blocks retain positive value.
PDF arXiv
arXiv stat.APOct 5
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Barsha Halder, Jeffrey A. Thompson
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric joint models, however, rely on fixed parametric assumptions, making them susceptible to bias under model misspecification and smaller sample sizes. We propose a deep joint model, DeepAJM, that does not require any parametric assumptions, while retaining a partially interpretable, per-longitudinal-outcome association structure. The joint model uses an encoder-decoder (sequence-to-sequence) architecture to learn the latent structure in patients' time-varying covariate trajectories. The model links the longitudinal processes to the survival processes through a learned interpretable association structure, in which each longitudinal output from the decoder gets remodulated by baseline covariates before it contributes to the risk scores from the survival head of the architecture. The model was evaluated on three datasets ( a cardiovascular-disease EHR cohort, a primary biliary cirrhosis (PBC2) dataset, and a simulated dataset) against a classical parametric joint model, TransformerJM,…
PDF arXiv
arXiv stat.MLOct 5
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Perrine Chassat, Agathe Guilloux
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of patient-level clinical data requires jointly modeling heterogeneous static covariates, irregularly sampled longitudinal trajectories, and informative observation times - three tightly coupled components in practice yet rarely addressed together. We propose HyperNSDE, a continuous-time generative model that conditions a latent Neural SDE on static patient representations through a hypernetwork, allowing baseline characteristics to shape trajectory evolution beyond the initial condition without requiring a trajectory encoder, while stochastic latent dynamics capture realistic variability in generated paths. Observation times are modeled jointly through a latent-state-dependent intensity process, and training on irregular stochastic paths is stabilized via a deterministic-stochastic path decomposition with a non-adversarial signature-kernel objective. Experiments on simulated and real clinical datasets show improved observation-time fidelity and competitive performance, while matched-grid analyses reveal that…
PDF arXiv
arXiv cs.CVOct 5
SimCortex v2: Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections
Kaveh Moradkhani, Sylvain Bouix
Reconstructing cortical WM and pial surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based neuroanatomical analysis, yet remains challenging because the cortex is thin and tightly folded. Reconstruction methods can produce geometric artifacts such as mesh self-intersections and collisions between cortical surfaces, and although recent deep learning methods have reduced reconstruction time from hours to minutes, these artifacts persist. We propose SimCortex v2, a deep learning framework for simultaneous reconstruction of the left and right WM and pial surfaces from T1-weighted MRI. SimCortex v2 estimates topologically correct initial surfaces from a volumetric segmentation and refines all four jointly using multi-scale stationary velocity fields predicted by a ribbon-conditioned, U-Net-like network. We evaluated SimCortex v2 on 560 cases from 14 cohorts, thirteen of them unseen during training, spanning ages 6-89, healthy and clinical populations, and scanners from three vendors. SimCortex v2 matched the surface-distance accuracy of the strongest baseline (average symmetric surface distance 0.253 mm) while showing no detected inter-surface…
PDF arXiv
arXiv cs.CVOct 5
Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings
Simon Hadush Nrea, Filimon Gidey Gebremichael, Gebrekirstos Hagos Gebrekirstos, Yaecob Girmay Gezahegn
Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology expertise and fragmented clinical data systems. Although deep learning models have demonstrated strong performance in mammographic analysis, most rely solely on imaging data and are trained on Western populations, limiting their applicability in African healthcare settings. This paper presents a Hybrid Cross-Modal Attention Network (HCMAN) that integrates mammogram images with structured clinical data using transformer-based cross-modal attention mechanisms. The model was developed and validated using a locally collected dataset of 2,560 mammogram images from 1,024 patients across four Ethiopian referral hospitals, with biopsy-confirmed ground truth labels. The proposed framework achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, significantly outperforming image-only baselines. The system demonstrates robustness to low-quality images typical of resource-limited settings, with only 3.2% performance degradation compared to 8.7% for image-only models. Cross-modal attention analysis reveals…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image395 papers54 this week
clinical reasoning · question answering · language models202 papers35 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers19 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers13 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 5
Adaptive Gait Biofeedback With Participant-Held-Out Modeling and Participant-Specific Updating in Chronic Ankle Instability
Jaeyoon Kim, Veronika Lebisova, Jeniya Sultana, Jaeyoung Cho, Jaeho Jang
Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human response. We evaluated a temporal convolutional classifier on protocol-defined, angle-derived GOOD/BAD gait-cycle labels using participant-held-out leave-one-subject-out (LOSO) cross-validation in 20 participants. Seven participants in the adaptive-intervention group completed nine sessions over three weeks, with one motion-capture recording analyzed per session. Models updated after failed sessions were compared offline with their parent models on the same-session validation subset used for candidate selection and the first subsequent adaptive-session recording. Frontal-plane ankle angle was compared between the adaptive group and 10 sequentially enrolled controls at Baseline, Post, and 7-day Retention. Across 20 held-out folds, mean fold-level area under the receiver operating characteristic curve (AUROC) was 0.948, sensitivity for angle-threshold-exceeding BAD cycles was 0.941, and specificity for angle-threshold-meeting GOOD cycles was 0.366. Mean BAD-class F1 was higher in candidate models by 0.187 on the same-session subset and…
PDF arXiv
arXiv cs.LGOct 5
Benchmarking Label-Revealed Online Updates for EEG BCI Decoding
Bogdan Kozyrskiy, Artem Grachev, Abraham I. Camelo Guerrero
Electroencephalography (EEG) signals drift over time, which can cause static brain-computer interface (BCI) models to degrade in practice. We present a benchmark for online adaptation and compare two widely used pipeline families, Common Spatial Patterns (CSP) and Riemannian covariance-based methods, under time-ordered prequential (test-then-train) evaluation. We examine (i) which pipelines benefit most from label-revealed updates, (ii) whether controlled forgetting of older data improves robustness, and (iii) how a minimal-calibration cold start compares with starting from a pretrained model. Across four datasets (three motor-imagery datasets and one movement-decoding dataset), label-revealed online updates improve 13 of 14 model/dataset pairs on the two largest streams, with relative accuracy gains of up to about 18% over a frozen model. A Shapley-based data-valuation analysis over temporal blocks assigns the largest mean value to the most recent block in each of the three analyzed datasets, while older blocks retain positive value.
PDF arXiv
arXiv stat.APOct 5
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Barsha Halder, Jeffrey A. Thompson
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric joint models, however, rely on fixed parametric assumptions, making them susceptible to bias under model misspecification and smaller sample sizes. We propose a deep joint model, DeepAJM, that does not require any parametric assumptions, while retaining a partially interpretable, per-longitudinal-outcome association structure. The joint model uses an encoder-decoder (sequence-to-sequence) architecture to learn the latent structure in patients' time-varying covariate trajectories. The model links the longitudinal processes to the survival processes through a learned interpretable association structure, in which each longitudinal output from the decoder gets remodulated by baseline covariates before it contributes to the risk scores from the survival head of the architecture. The model was evaluated on three datasets ( a cardiovascular-disease EHR cohort, a primary biliary cirrhosis (PBC2) dataset, and a simulated dataset) against a classical parametric joint model, TransformerJM,…
PDF arXiv
arXiv stat.MLOct 5
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Perrine Chassat, Agathe Guilloux
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of patient-level clinical data requires jointly modeling heterogeneous static covariates, irregularly sampled longitudinal trajectories, and informative observation times - three tightly coupled components in practice yet rarely addressed together. We propose HyperNSDE, a continuous-time generative model that conditions a latent Neural SDE on static patient representations through a hypernetwork, allowing baseline characteristics to shape trajectory evolution beyond the initial condition without requiring a trajectory encoder, while stochastic latent dynamics capture realistic variability in generated paths. Observation times are modeled jointly through a latent-state-dependent intensity process, and training on irregular stochastic paths is stabilized via a deterministic-stochastic path decomposition with a non-adversarial signature-kernel objective. Experiments on simulated and real clinical datasets show improved observation-time fidelity and competitive performance, while matched-grid analyses reveal that…
PDF arXiv
arXiv cs.CVOct 5
SimCortex v2: Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections
Kaveh Moradkhani, Sylvain Bouix
Reconstructing cortical WM and pial surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based neuroanatomical analysis, yet remains challenging because the cortex is thin and tightly folded. Reconstruction methods can produce geometric artifacts such as mesh self-intersections and collisions between cortical surfaces, and although recent deep learning methods have reduced reconstruction time from hours to minutes, these artifacts persist. We propose SimCortex v2, a deep learning framework for simultaneous reconstruction of the left and right WM and pial surfaces from T1-weighted MRI. SimCortex v2 estimates topologically correct initial surfaces from a volumetric segmentation and refines all four jointly using multi-scale stationary velocity fields predicted by a ribbon-conditioned, U-Net-like network. We evaluated SimCortex v2 on 560 cases from 14 cohorts, thirteen of them unseen during training, spanning ages 6-89, healthy and clinical populations, and scanners from three vendors. SimCortex v2 matched the surface-distance accuracy of the strongest baseline (average symmetric surface distance 0.253 mm) while showing no detected inter-surface…
PDF arXiv
arXiv cs.CVOct 5
Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings
Simon Hadush Nrea, Filimon Gidey Gebremichael, Gebrekirstos Hagos Gebrekirstos, Yaecob Girmay Gezahegn
Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology expertise and fragmented clinical data systems. Although deep learning models have demonstrated strong performance in mammographic analysis, most rely solely on imaging data and are trained on Western populations, limiting their applicability in African healthcare settings. This paper presents a Hybrid Cross-Modal Attention Network (HCMAN) that integrates mammogram images with structured clinical data using transformer-based cross-modal attention mechanisms. The model was developed and validated using a locally collected dataset of 2,560 mammogram images from 1,024 patients across four Ethiopian referral hospitals, with biopsy-confirmed ground truth labels. The proposed framework achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, significantly outperforming image-only baselines. The system demonstrates robustness to low-quality images typical of resource-limited settings, with only 3.2% performance degradation compared to 8.7% for image-only models. Cross-modal attention analysis reveals…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 59m ago

Image segmentation

Found by the topic model: image segmentation, clinical reasoning, eeg foundation models.

169papers this week−19.5% vs last week

All papers · 1.1k With code · 4

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1k papers this month

Open problems in Image segmentation

Lack of benchmarks for agentic evidence-seeking in medical imaging1 paper raises it · 1 addresses it
Current benchmarks assess reasoning over preselected evidence rather than the ability to autonomously seek and integrate longitudinal imaging and clinical records.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Scaling parameter-efficient fine-tuning for large medical models1 paper raises it · 1 addresses it
Neural data is costly and limited, making full fine-tuning or training from scratch impractical for large models in medical tasks like brain encoding.
- Scaling Parameter-Efficient Fine-Tuning for Brain Encodingraises · addresses
Generating coherent gigapixel whole-slide images1 paper raises it · 1 addresses it
Existing histopathology generative models operate at patch scale, while clinical supervision and diagnoses operate at whole-slide level, creating a mismatch.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Anatomically unsupported detail in generative super-resolution for CT1 paper raises it · 1 addresses it
Generative super-resolution can produce visually plausible but anatomically incorrect vascular structures in CT images.
- VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolutionraises · addresses
Frozen models unable to learn from deployment experience1 paper raises it · 1 addresses it
Deployed medical AI models are typically frozen, preventing them from adapting to new clinical evidence or updated guidelines.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses

Named by an AI from these papers' abstracts.