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Open problems in Image segmentation

Lack of whole-slide image generation for histopathology1 paper raises it · 1 addresses it
Current generative models only produce small patches, but clinical labels and reports operate at the whole-slide level, limiting alignment with real-world supervision.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Limited longitudinal multimodal reasoning benchmarks1 paper raises it · 1 addresses it
Most medical benchmarks assess reasoning over preselected evidence, not the ability to seek and integrate evidence across longitudinal clinical records and imaging.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Frozen models cannot learn from deployment experience1 paper raises it · 1 addresses it
Deployed models in medicine do not adapt to new clinical evidence or updated guidelines without retraining, which is often impractical.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses
Benchmark misalignment with real-world clinical use1 paper raises it · 1 addresses it
Public benchmarks often do not reflect the tasks, intents, or constraints of actual clinician queries, reducing their predictive value for deployment performance.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
Sensitivity to incidental information in clinical reasoning1 paper raises it · 1 addresses it
Models may incorporate or misattribute irrelevant details (e.g., small talk, background noise) in clinical documentation or reasoning, risking contamination.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

image segmentation · volumetric · medical image400 papers59 this week
clinical reasoning · question answering · language models204 papers36 this week
radiology report generation · medical vision language · vision language models119 papers30 this week
patient history · clinical data · health records109 papers27 this week
eeg foundation models · electroencephalography eeg · eeg foundation153 papers22 this week
electrocardiogram ecg · electrocardiogram · ecgs145 papers17 this week

Topics in this field

Active contributors · 90 days

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Activity · 12 weeks

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All papers in Research

arXiv cs.CVOct 5
Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology
Tuo Yin, Jennifer Dhont
Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, the development and validation of deep learning algorithms still require annotated data, a costly procedure involving expert pathologists who already face critical workforce shortages. Existing coreset selection methods to optimize annotation efforts currently all rely on hyperparameters tuned on natural-image benchmarks that do not transfer to histopathology and are cumbersome to use in clinical practice. In this study, we present GCcore, a novel label-free coreset selection method that embeds every image of a dataset with any pathology foundation model and greedily selects the samples that collectively maximize the global coverage of the embedding space. The proposed method provides a lower-bound guarantee on the global coverage of the returned coreset for any coreset size, while being completely hyperparameter-free and deterministic. We demonstrate GCcore's superior performance over 14 baselines including state-of-the-art methods across 10 tasks and datasets spanning whole slide image classification, tile classification, and…
PDF arXiv
arXiv cs.CVOct 5
Decomposition-Guided Curvelet Thresholding for Sharp-to-Soft CT Kernel Conversion
Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski, Adam Piorkowski
Image denoising is a crucial task in image processing, focused on improving image quality by minimizing noise while maintaining essential structural elements. This study presents a hybrid denoising framework that combines several decomposition techniques, including empirical mode decomposition (EMD), variational mode decomposition (VMD), multichannel EMD (MEMD), and bidimensional EMD (BEMD), with curvelet transform thresholding. Each decomposition mode undergoes processing through both soft and hard thresholding, and the denoised modes are combined to rebuild the final image. Comprehensive evaluations of standard CT image datasets reconstructed with various kernels (B50, B46, B41, B36) reveal substantial enhancements in denoising efficacy. VMD consistently achieves the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), signifying exceptional noise reduction and feature preservation. The study analyses the trade-offs between soft and hard thresholding: soft thresholding maintains intricate visual details, whilst harsh thresholding provides enhanced noise reduction. The suggested method surpasses traditional techniques in both reference and…
PDF arXiv
arXiv cs.LGOct 5
Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu Palaniswami
Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive video-electroencephalography (VEEG) monitoring, requires specialized expertise and is limited to in-hospital settings, and intrusive in nature. Seizure diaries, on the other hand, suffer from unreliability due to under-reporting, leading to incorrect therapeutic decisions. Wearable non-invasive seizure detection may offer a more tolerable and feasible solution for long-term ambulatory monitoring. This study explores a wearable remote monitoring system utilizing a single wrist-worn accelerometer device and capable of detecting multiple types of seizures, including shorter duration events. We enrolled 79 patients under video-electroencephalography monitoring to wear accelerometer devices and collect data. Concurrent VEEG recordings were reviewed by board-certified epileptologists to produce annotations, including seizure onset, offset, and seizure type. Using this data, we constructed a deep neural network based on the time-series ResNet architecture, which could discriminate among seizure and non-seizure events. Our…
PDF arXiv
arXiv cs.LGOct 5
CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI
Boseong Kim, Haejun Chung, Ikbeom Jang
Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise graphs, thereby obscuring condition-specific multi-ROI organization. We introduce CoHyFuse, a condition-aware ROI-centered hypergraph framework that constructs a task-state-specific incidence matrix from condition-wise functional connectivity (FC)-profile embeddings, allowing the same ROI to form different multi-ROI hyperedges across task phases. Condition-specific neighborhood sizes $K_q$ further adapt the hyperedge scale to each task state, and the resulting condition embeddings are fused with a complementary whole-session FC branch for prediction. In the AABC cohort (N=1,074), CoHyFuse achieved the best mean out-of-fold predictive performance among evaluated baselines on FACENAME Fluid Cognition Composite (FCC) prediction (7.83$\pm$0.10 MAE, 0.439$\pm$0.026 $R^2$) and VISMOTOR age prediction (7.52$\pm$0.37 MAE, 0.592$\pm$0.022 $R^2$). In an auxiliary CMI-HBN attention-deficit/hyperactivity disorder (ADHD) classification benchmark (N=223), CoHyFuse obtained 72.0$\pm$2.1\% macro-AUC and 74.2$\pm$2.9\%…
PDF arXiv
arXiv cs.CVOct 5
End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction
Zeyu Jia, Xin Ming
Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic assessment. Conventional voxel segmentation models often sever delicate bifurcations, while heuristic Euclidean Minimum Spanning Trees introduce non-anatomical shortcuts. Moreover, linear Poiseuille flow neglects quadratic kinetic dissipation across arterial narrowings, underestimating ischemia. We formulate an end-to-end framework decoupling continuous geometric arborescence generation from non-linear hemodynamics. First, an autonomous 3D Ostium Landmark Localization Head with dual-sinus query channels and spherical-gated refinement eliminates centerline seeding dependency, achieving cohort mean localization error of 7.63 mm (7.43 mm LCA, 7.83 mm RCA; 71.4% <= 8.0 mm) from raw contrast context. Second, a Spatially-Grounded Deformable Step Flow Architecture queries continuous 3D feature pyramids via trilinear sampling, sequentially generating trajectories with anchor boundary enforcement (X(0) = P_start). Third, a Top-Down Recursive Arborescence State Machine detects bifurcation peaks via Tree-NMS and parameterizes predecessor parent…
PDF arXiv
arXiv cs.CVOct 5
fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification
Juliana Mantebea Danso, Enoch Opanin Gyamfi, Mylene C. Q. Farias
Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing methods fuse images, BOLD-based functional connectivity, and phenotypic data modalities. Unlike other medical imaging datasets, rs-fMRI datasets rarely include a text modality, so they are generated from phenotypic data or BOLD activations. These text generation methods rely on fixed assumptions for subjects, sites, devices, and protocols, leading to poor generalization across datasets. We propose fMRI-TAMCL, a text-anchored multimodal contrastive learning framework that integrates fMRI images, sparse FC, and generated subject-specific text. Its Subject-Adaptive Threshold Derivation module generates BOLD activation text, while Feature-Value Serialization module generates phenotypic text. All three modalities are encoded as clustered graphs, projected onto a shared unit hypersphere space, aligned using pairwise, text-anchored supervised contrastive learning, and fused with attention. fMRI-TAMCL proves its generalization capability across five datasets outperforming 29 baselines with 78.6%-86.4% accuracy in downstream classification.
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image400 papers59 this week
clinical reasoning · question answering · language models204 papers36 this week
radiology report generation · medical vision language · vision language models119 papers30 this week
patient history · clinical data · health records109 papers27 this week
eeg foundation models · electroencephalography eeg · eeg foundation153 papers22 this week
electrocardiogram ecg · electrocardiogram · ecgs145 papers17 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CVOct 5
Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology
Tuo Yin, Jennifer Dhont
Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, the development and validation of deep learning algorithms still require annotated data, a costly procedure involving expert pathologists who already face critical workforce shortages. Existing coreset selection methods to optimize annotation efforts currently all rely on hyperparameters tuned on natural-image benchmarks that do not transfer to histopathology and are cumbersome to use in clinical practice. In this study, we present GCcore, a novel label-free coreset selection method that embeds every image of a dataset with any pathology foundation model and greedily selects the samples that collectively maximize the global coverage of the embedding space. The proposed method provides a lower-bound guarantee on the global coverage of the returned coreset for any coreset size, while being completely hyperparameter-free and deterministic. We demonstrate GCcore's superior performance over 14 baselines including state-of-the-art methods across 10 tasks and datasets spanning whole slide image classification, tile classification, and…
PDF arXiv
arXiv cs.CVOct 5
Decomposition-Guided Curvelet Thresholding for Sharp-to-Soft CT Kernel Conversion
Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski, Adam Piorkowski
Image denoising is a crucial task in image processing, focused on improving image quality by minimizing noise while maintaining essential structural elements. This study presents a hybrid denoising framework that combines several decomposition techniques, including empirical mode decomposition (EMD), variational mode decomposition (VMD), multichannel EMD (MEMD), and bidimensional EMD (BEMD), with curvelet transform thresholding. Each decomposition mode undergoes processing through both soft and hard thresholding, and the denoised modes are combined to rebuild the final image. Comprehensive evaluations of standard CT image datasets reconstructed with various kernels (B50, B46, B41, B36) reveal substantial enhancements in denoising efficacy. VMD consistently achieves the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), signifying exceptional noise reduction and feature preservation. The study analyses the trade-offs between soft and hard thresholding: soft thresholding maintains intricate visual details, whilst harsh thresholding provides enhanced noise reduction. The suggested method surpasses traditional techniques in both reference and…
PDF arXiv
arXiv cs.LGOct 5
Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu Palaniswami
Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive video-electroencephalography (VEEG) monitoring, requires specialized expertise and is limited to in-hospital settings, and intrusive in nature. Seizure diaries, on the other hand, suffer from unreliability due to under-reporting, leading to incorrect therapeutic decisions. Wearable non-invasive seizure detection may offer a more tolerable and feasible solution for long-term ambulatory monitoring. This study explores a wearable remote monitoring system utilizing a single wrist-worn accelerometer device and capable of detecting multiple types of seizures, including shorter duration events. We enrolled 79 patients under video-electroencephalography monitoring to wear accelerometer devices and collect data. Concurrent VEEG recordings were reviewed by board-certified epileptologists to produce annotations, including seizure onset, offset, and seizure type. Using this data, we constructed a deep neural network based on the time-series ResNet architecture, which could discriminate among seizure and non-seizure events. Our…
PDF arXiv
arXiv cs.LGOct 5
CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI
Boseong Kim, Haejun Chung, Ikbeom Jang
Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise graphs, thereby obscuring condition-specific multi-ROI organization. We introduce CoHyFuse, a condition-aware ROI-centered hypergraph framework that constructs a task-state-specific incidence matrix from condition-wise functional connectivity (FC)-profile embeddings, allowing the same ROI to form different multi-ROI hyperedges across task phases. Condition-specific neighborhood sizes $K_q$ further adapt the hyperedge scale to each task state, and the resulting condition embeddings are fused with a complementary whole-session FC branch for prediction. In the AABC cohort (N=1,074), CoHyFuse achieved the best mean out-of-fold predictive performance among evaluated baselines on FACENAME Fluid Cognition Composite (FCC) prediction (7.83$\pm$0.10 MAE, 0.439$\pm$0.026 $R^2$) and VISMOTOR age prediction (7.52$\pm$0.37 MAE, 0.592$\pm$0.022 $R^2$). In an auxiliary CMI-HBN attention-deficit/hyperactivity disorder (ADHD) classification benchmark (N=223), CoHyFuse obtained 72.0$\pm$2.1\% macro-AUC and 74.2$\pm$2.9\%…
PDF arXiv
arXiv cs.CVOct 5
End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction
Zeyu Jia, Xin Ming
Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic assessment. Conventional voxel segmentation models often sever delicate bifurcations, while heuristic Euclidean Minimum Spanning Trees introduce non-anatomical shortcuts. Moreover, linear Poiseuille flow neglects quadratic kinetic dissipation across arterial narrowings, underestimating ischemia. We formulate an end-to-end framework decoupling continuous geometric arborescence generation from non-linear hemodynamics. First, an autonomous 3D Ostium Landmark Localization Head with dual-sinus query channels and spherical-gated refinement eliminates centerline seeding dependency, achieving cohort mean localization error of 7.63 mm (7.43 mm LCA, 7.83 mm RCA; 71.4% <= 8.0 mm) from raw contrast context. Second, a Spatially-Grounded Deformable Step Flow Architecture queries continuous 3D feature pyramids via trilinear sampling, sequentially generating trajectories with anchor boundary enforcement (X(0) = P_start). Third, a Top-Down Recursive Arborescence State Machine detects bifurcation peaks via Tree-NMS and parameterizes predecessor parent…
PDF arXiv
arXiv cs.CVOct 5
fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification
Juliana Mantebea Danso, Enoch Opanin Gyamfi, Mylene C. Q. Farias
Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing methods fuse images, BOLD-based functional connectivity, and phenotypic data modalities. Unlike other medical imaging datasets, rs-fMRI datasets rarely include a text modality, so they are generated from phenotypic data or BOLD activations. These text generation methods rely on fixed assumptions for subjects, sites, devices, and protocols, leading to poor generalization across datasets. We propose fMRI-TAMCL, a text-anchored multimodal contrastive learning framework that integrates fMRI images, sparse FC, and generated subject-specific text. Its Subject-Adaptive Threshold Derivation module generates BOLD activation text, while Feature-Value Serialization module generates phenotypic text. All three modalities are encoded as clustered graphs, projected onto a shared unit hypersphere space, aligned using pairwise, text-anchored supervised contrastive learning, and fused with attention. fMRI-TAMCL proves its generalization capability across five datasets outperforming 29 baselines with 78.6%-86.4% accuracy in downstream classification.
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 23m ago

Image segmentation

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

191papers this week−9.0% vs last week

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1k papers this month

Open problems in Image segmentation

Lack of whole-slide image generation for histopathology1 paper raises it · 1 addresses it
Current generative models only produce small patches, but clinical labels and reports operate at the whole-slide level, limiting alignment with real-world supervision.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Limited longitudinal multimodal reasoning benchmarks1 paper raises it · 1 addresses it
Most medical benchmarks assess reasoning over preselected evidence, not the ability to seek and integrate evidence across longitudinal clinical records and imaging.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Frozen models cannot learn from deployment experience1 paper raises it · 1 addresses it
Deployed models in medicine do not adapt to new clinical evidence or updated guidelines without retraining, which is often impractical.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses
Benchmark misalignment with real-world clinical use1 paper raises it · 1 addresses it
Public benchmarks often do not reflect the tasks, intents, or constraints of actual clinician queries, reducing their predictive value for deployment performance.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
Sensitivity to incidental information in clinical reasoning1 paper raises it · 1 addresses it
Models may incorporate or misattribute irrelevant details (e.g., small talk, background noise) in clinical documentation or reasoning, risking contamination.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises · addresses

Named by an AI from these papers' abstracts.