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Open problems in Image segmentation

Lack of benchmarks for real-world clinical deployment1 paper raises it · 0 address it
Current benchmarks do not reflect how clinicians actually use AI systems, limiting their predictive value for real-world performance.
- Clinician use of language models diverges from how the models are evaluatedraises
Handling gigapixel whole-slide images in segmentation1 paper raises it · 1 addresses it
Existing models operate at patch scale, but clinical supervision and labels are at the whole-slide level, creating a mismatch.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Temporal modeling of multimodal clinical trajectories1 paper raises it · 1 addresses it
Accurate risk prediction requires modeling irregular, heterogeneous clinical data over time, which current approaches often overlook.
- TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Predictionraises · addresses
Resistance to incidental information in clinical reasoning1 paper raises it · 0 address it
Models may misattribute or be distracted by irrelevant information in clinical documentation, leading to errors.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises
Learning from deployment experience without retraining1 paper raises it · 1 addresses it
Frozen models cannot adapt to new clinical evidence or updated guidelines without access to weights or additional training.
- 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 image409 papers42 this week
radiology report generation · medical vision language · vision language models133 papers35 this week
clinical reasoning · question answering · language models210 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation158 papers20 this week
patient history · clinical data · health records112 papers20 this week
electrocardiogram ecg · electrocardiogram · ecgs147 papers14 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv cs.AISep 26
Separating Diagnosis from Disease Representation: Dual-View EEG Learning with Neural-Dynamics-Guided Deformation
Jiaying Wang, Shouqian Shi, Yutong Chen, Xu Yang, Jie Chen +3
Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence without anatomy, source-space models give anatomy at a loss of predictive signal, and post-hoc attributions stay outside the prediction. We separate the two instead of forcing them into one representation, and propose DMD-EEG (Dual-view Multiscale Deformation for EEG), which keeps a fixed scalp spectral expert for diagnosis and models the source-space disease-related representation as a low-rank, sparse, iterative deformation of a healthy neural-dynamics prior in a $46$-region-of-interest (ROI) $\times$ $5$-frequency $\times$ $4$-lag (autocorrelation-timescale) space. The two experts meet only at a fixed decision level, so the source state is architecturally separate from the scalp expert. Across major depressive disorder (MDD), first-episode psychosis (FEP), and Parkinson's disease (PD), decision-level fusion matches the strongest single expert on MDD and FEP and exceeds the source…
PDF arXiv
arXiv cs.LGSep 26
Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions
Viktoriya Bu-Dager, Silvia Cirstea
Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning framework to predict log-transformed fasting blood glucose using metabolic, hormonal, lifestyle, demographic, nutritional, and circadian variables from the National Health and Nutrition Examination Survey 2017--2020 pre-pandemic dataset. After merging multiple NHANES sub-datasets, data processing used a leakage-resistant pipeline in which imputation, scaling, and one-hot encoding were performed only after dataset splitting and within training folds. Elastic Net, LASSO, and XGBoost models were evaluated using 94 candidate predictors and engineered circadian interaction terms. Performance was assessed using mean absolute error, root mean squared error, coefficient of determination, calibration, and Shapley Additive Explanations. The final interaction-augmented XGBoost model achieved strong performance on the independent test set, with a mean absolute error of 0.0804, a root mean squared error of 0.1148, and a coefficient of determination of 0.7761, using 10 predictors. Glycohemoglobin was the dominant predictor, followed by insulin,…
PDF arXiv
arXiv cs.CVSep 26
EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding
Gujie Shao, Zixun Xie, Xuechun Xing, Ruixiang Wang, Ziyun Lan +5
Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging remain insufficiently characterized. Existing ophthalmic datasets are typically designed for individual diseases or specialized tasks, making it difficult to systematically evaluate whether VLMs can move beyond disease recognition toward comparative reasoning and fine-grained spatial grounding. We introduce EyeVQA, a unified visual question answering benchmark for comprehensive evaluation of ophthalmic VLMs. EyeVQA is constructed from 21 available ophthalmic datasets and contains 20,000 question-answer pairs spanning six disease groups and seven question types: Single-Choice, Multi-Select, Variable-Select, True-False, Ranking, Point Location, and Bounding Box. Gold answers are deterministically derived from source-provided diagnoses, severity grades, clinical findings, segmentation masks, bounding boxes, and anatomical landmarks, enabling reproducible evaluation without relying on model-generated annotations. Notably, 44.5% of the questions require reasoning across multiple images, extending evaluation beyond conventional single-image medical…
PDF arXiv
arXiv cs.CVSep 26
OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI
Zhengyi Lu, Ming Lu, Chongyu Qu, Junchao Zhu, Junlin Guo +9
Metal implants corrupt MRI measurements throughout $k$-space, yet existing accelerated MRI methods assume clean data and most metal artifact reduction approaches assume fully sampled acquisitions. No public dataset provides paired $k$-space and images with and without metal for the same anatomy, and no framework jointly addresses artifact-aware acquisition and reconstruction across sampling trajectories. We present OpenMASC, an open-source pipeline covering the full workflow from data generation to deployment. A physics-based data generation module converts public CT volumes into paired clean and metal-corrupted MRI data in both Cartesian and radial formats. MA-VarNet, an unrolled reconstruction network with a per-cascade DC Rectifier, corrects artifacts that data-consistency steps reintroduce from corrupted measurements. A reinforcement learning agent actively selects $k$-space readouts and co-trains with the reconstruction network through a decoupled three-stage procedure. The framework is trajectory-agnostic except for the data-consistency operator, supporting both Cartesian and radial acquisition without architectural changes. Experiments on two datasets at $4\times$ and…
PDF arXiv
arXiv cs.CVSep 26
FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation
Truong Viet Vu, Nguyen Phuc Nguyen, Dang Thi Thu Hang, Tran Thien Thanh, Vo Nguyen Quoc Bao +2
Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under limited annotation, cross-cohort variation, and severe target sparsity. We propose FSS-UBrain, a region-wise one-shot segmentation framework that uses a labeled positive support slice to condition binary query segmentation separately for WT, TC, and ET. Support-derived foreground and background descriptors guide query-feature adaptation, bottleneck interaction, decoder-side reconstruction, and boundary refinement. Episodic training additionally incorporates hard-negative and fully negative queries with empty-query regularization to suppress spurious foreground activation when the selected region is absent. Although inference operates on two-dimensional support--query slice pairs, checkpoint selection, threshold calibration, and final evaluation are performed after volumetric reconstruction. FSS-UBrain is evaluated on a held-out BraTS 2020 split and under target-supported cross-cohort protocols on BraTS 2023 and BraTS-Africa. Cases used as target support are excluded from the query cohorts, and no target-domain fine-tuning or…
PDF arXiv
arXiv cs.CVSep 26
Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas
Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar, Brent A. Orr
DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational models typically analyze methylation and copy number variation (CNV) data separately, failing to capture the complementary information their integration could provide. Moreover, current classification models lack mechanisms for within-class risk assessment analogous to traditional tumor grading, and no established explainability method can attribute classification decisions to specific genomic loci. In this paper, we present MOXAI (Multi-Omics Based Explainable AI), a deep learning framework that integrates DNA methylation and copy number data from methylation arrays to classify molecular subtypes of adult-type diffuse gliomas, alongside single-modality variants for comparison. Using a cohort from The Cancer Genome Atlas (TCGA), we trained ResNet50, DINOv2, and Graph Attention Network (GAT) models on methylation data alone, copy number data alone, and combined multimodal data. We further developed explainable AI (XAI) methods based on class activation maps (CAMs) and gradient-weighted CAM (Grad-CAM) to identify the specific CpG sites,…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image409 papers42 this week
radiology report generation · medical vision language · vision language models133 papers35 this week
clinical reasoning · question answering · language models210 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation158 papers20 this week
patient history · clinical data · health records112 papers20 this week
electrocardiogram ecg · electrocardiogram · ecgs147 papers14 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AISep 26
Separating Diagnosis from Disease Representation: Dual-View EEG Learning with Neural-Dynamics-Guided Deformation
Jiaying Wang, Shouqian Shi, Yutong Chen, Xu Yang, Jie Chen +3
Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence without anatomy, source-space models give anatomy at a loss of predictive signal, and post-hoc attributions stay outside the prediction. We separate the two instead of forcing them into one representation, and propose DMD-EEG (Dual-view Multiscale Deformation for EEG), which keeps a fixed scalp spectral expert for diagnosis and models the source-space disease-related representation as a low-rank, sparse, iterative deformation of a healthy neural-dynamics prior in a $46$-region-of-interest (ROI) $\times$ $5$-frequency $\times$ $4$-lag (autocorrelation-timescale) space. The two experts meet only at a fixed decision level, so the source state is architecturally separate from the scalp expert. Across major depressive disorder (MDD), first-episode psychosis (FEP), and Parkinson's disease (PD), decision-level fusion matches the strongest single expert on MDD and FEP and exceeds the source…
PDF arXiv
arXiv cs.LGSep 26
Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions
Viktoriya Bu-Dager, Silvia Cirstea
Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning framework to predict log-transformed fasting blood glucose using metabolic, hormonal, lifestyle, demographic, nutritional, and circadian variables from the National Health and Nutrition Examination Survey 2017--2020 pre-pandemic dataset. After merging multiple NHANES sub-datasets, data processing used a leakage-resistant pipeline in which imputation, scaling, and one-hot encoding were performed only after dataset splitting and within training folds. Elastic Net, LASSO, and XGBoost models were evaluated using 94 candidate predictors and engineered circadian interaction terms. Performance was assessed using mean absolute error, root mean squared error, coefficient of determination, calibration, and Shapley Additive Explanations. The final interaction-augmented XGBoost model achieved strong performance on the independent test set, with a mean absolute error of 0.0804, a root mean squared error of 0.1148, and a coefficient of determination of 0.7761, using 10 predictors. Glycohemoglobin was the dominant predictor, followed by insulin,…
PDF arXiv
arXiv cs.CVSep 26
EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding
Gujie Shao, Zixun Xie, Xuechun Xing, Ruixiang Wang, Ziyun Lan +5
Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging remain insufficiently characterized. Existing ophthalmic datasets are typically designed for individual diseases or specialized tasks, making it difficult to systematically evaluate whether VLMs can move beyond disease recognition toward comparative reasoning and fine-grained spatial grounding. We introduce EyeVQA, a unified visual question answering benchmark for comprehensive evaluation of ophthalmic VLMs. EyeVQA is constructed from 21 available ophthalmic datasets and contains 20,000 question-answer pairs spanning six disease groups and seven question types: Single-Choice, Multi-Select, Variable-Select, True-False, Ranking, Point Location, and Bounding Box. Gold answers are deterministically derived from source-provided diagnoses, severity grades, clinical findings, segmentation masks, bounding boxes, and anatomical landmarks, enabling reproducible evaluation without relying on model-generated annotations. Notably, 44.5% of the questions require reasoning across multiple images, extending evaluation beyond conventional single-image medical…
PDF arXiv
arXiv cs.CVSep 26
OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI
Zhengyi Lu, Ming Lu, Chongyu Qu, Junchao Zhu, Junlin Guo +9
Metal implants corrupt MRI measurements throughout $k$-space, yet existing accelerated MRI methods assume clean data and most metal artifact reduction approaches assume fully sampled acquisitions. No public dataset provides paired $k$-space and images with and without metal for the same anatomy, and no framework jointly addresses artifact-aware acquisition and reconstruction across sampling trajectories. We present OpenMASC, an open-source pipeline covering the full workflow from data generation to deployment. A physics-based data generation module converts public CT volumes into paired clean and metal-corrupted MRI data in both Cartesian and radial formats. MA-VarNet, an unrolled reconstruction network with a per-cascade DC Rectifier, corrects artifacts that data-consistency steps reintroduce from corrupted measurements. A reinforcement learning agent actively selects $k$-space readouts and co-trains with the reconstruction network through a decoupled three-stage procedure. The framework is trajectory-agnostic except for the data-consistency operator, supporting both Cartesian and radial acquisition without architectural changes. Experiments on two datasets at $4\times$ and…
PDF arXiv
arXiv cs.CVSep 26
FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation
Truong Viet Vu, Nguyen Phuc Nguyen, Dang Thi Thu Hang, Tran Thien Thanh, Vo Nguyen Quoc Bao +2
Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under limited annotation, cross-cohort variation, and severe target sparsity. We propose FSS-UBrain, a region-wise one-shot segmentation framework that uses a labeled positive support slice to condition binary query segmentation separately for WT, TC, and ET. Support-derived foreground and background descriptors guide query-feature adaptation, bottleneck interaction, decoder-side reconstruction, and boundary refinement. Episodic training additionally incorporates hard-negative and fully negative queries with empty-query regularization to suppress spurious foreground activation when the selected region is absent. Although inference operates on two-dimensional support--query slice pairs, checkpoint selection, threshold calibration, and final evaluation are performed after volumetric reconstruction. FSS-UBrain is evaluated on a held-out BraTS 2020 split and under target-supported cross-cohort protocols on BraTS 2023 and BraTS-Africa. Cases used as target support are excluded from the query cohorts, and no target-domain fine-tuning or…
PDF arXiv
arXiv cs.CVSep 26
Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas
Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar, Brent A. Orr
DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational models typically analyze methylation and copy number variation (CNV) data separately, failing to capture the complementary information their integration could provide. Moreover, current classification models lack mechanisms for within-class risk assessment analogous to traditional tumor grading, and no established explainability method can attribute classification decisions to specific genomic loci. In this paper, we present MOXAI (Multi-Omics Based Explainable AI), a deep learning framework that integrates DNA methylation and copy number data from methylation arrays to classify molecular subtypes of adult-type diffuse gliomas, alongside single-modality variants for comparison. Using a cohort from The Cancer Genome Atlas (TCGA), we trained ResNet50, DINOv2, and Graph Attention Network (GAT) models on methylation data alone, copy number data alone, and combined multimodal data. We further developed explainable AI (XAI) methods based on class activation maps (CAMs) and gradient-weighted CAM (Grad-CAM) to identify the specific CpG sites,…
PDF arXiv

Activity · 12 weeks

Open problems in Image segmentation

Lack of benchmarks for real-world clinical deployment1 paper raises it · 0 address it
Current benchmarks do not reflect how clinicians actually use AI systems, limiting their predictive value for real-world performance.
- Clinician use of language models diverges from how the models are evaluatedraises
Handling gigapixel whole-slide images in segmentation1 paper raises it · 1 addresses it
Existing models operate at patch scale, but clinical supervision and labels are at the whole-slide level, creating a mismatch.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Temporal modeling of multimodal clinical trajectories1 paper raises it · 1 addresses it
Accurate risk prediction requires modeling irregular, heterogeneous clinical data over time, which current approaches often overlook.
- TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Predictionraises · addresses
Resistance to incidental information in clinical reasoning1 paper raises it · 0 address it
Models may misattribute or be distracted by irrelevant information in clinical documentation, leading to errors.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises
Learning from deployment experience without retraining1 paper raises it · 1 addresses it
Frozen models cannot adapt to new clinical evidence or updated guidelines without access to weights or additional training.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 15m ago

Image segmentation

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

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