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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 use longitudinal imaging and clinical records.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Gigapixel whole-slide image generation and segmentation1 paper raises it · 1 addresses it
Existing histopathology models operate at patch scale, but clinical supervision and diagnoses require whole-slide level coherence and fidelity.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Anatomically unsupported detail in generative super-resolution for CT1 paper raises it · 1 addresses it
Generative SR can produce visually plausible but anatomically incorrect vascular structures, leading to unreliable clinical interpretations.
- VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolutionraises · addresses
Mismatch between clinical use and evaluation benchmarks1 paper raises it · 1 addresses it
Benchmarks often fail to reflect real-world clinical queries, leading to poor generalization of model performance in deployment.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
Parameter-efficient fine-tuning for large-scale medical models1 paper raises it · 1 addresses it
Training or fully fine-tuning large models on limited neural or medical data is computationally expensive and often impractical.
- Scaling Parameter-Efficient Fine-Tuning for Brain Encodingraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

image segmentation · volumetric · medical image381 papers49 this week
clinical reasoning · question answering · language models195 papers36 this week
patient history · clinical data · health records104 papers26 this week
eeg foundation models · electroencephalography eeg · eeg foundation144 papers18 this week
radiology report generation · medical vision language · vision language models106 papers18 this week
electrocardiogram ecg · electrocardiogram · ecgs137 papers14 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv cs.AIOct 18
From Clinical Risk Prediction to Operational Decisions: An Uncertainty-Aware Framework for ICU Staffing
Jiayan Yang, Ad Feelders, Arno Siebes
Advances in electronic health records (EHR) and large-scale critical care databases have enabled the development of machine learning models for the early detection of patient deterioration. While these models often achieve strong predictive performance for outcomes such as sepsis and mortality, most studies evaluate them purely on discrimination metrics. This leaves a critical gap: how to systematically translate probabilistic clinical risk estimates into downstream operational decisions under real-world constraints, such as shift rigidity and hiring delays. This study proposes an uncertainty-aware anticipatory staffing framework that bridges short-term patient risk prediction and staff planning without altering the upstream predictive model. Instead of relying on naive deterministic aggregation or fixed safety buffers, the framework explicitly models dynamic predictive uncertainty and evaluates extreme shortage exposure using Conditional Value-at-Risk (CVaR). These aggregated, risk-calibrated demand signals are then embedded into an hourly rolling-horizon scheduling policy to generate delay-constrained staffing decisions. Evaluated on realistic trajectories from the MIMIC-IV…
PDF arXiv
arXiv cs.CVOct 18
SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation
Yang Hong, Lei Zhang, Zhihong Liu, Jianqing Mo, Xujiong Ye
To determine where computation is needed in volumetric segmentation networks, we analyze block-level redundancy and stage-wise token-mixer contribution. Centered Kernel Alignment reveals high input-output representational similarity within blocks of representative convolutional, hybrid, and Transformer-style segmentation networks. Similarity-guided pruning reduces their parameter counts by 39-75% while decreasing mean Dice by less than 0.4 percentage points. To examine token mixers within blocks, we introduce Anatomical Relation Distortion, which quantifies how bypassing a mixer distorts feature relations between labeled anatomical positions. Applied to an unpruned candidate architecture, Centered Kernel Alignment indicates high representational redundancy in intermediate encoder mixers, whereas Anatomical Relation Distortion shows that they contribute little and the bottleneck mixer most. These findings motivate SlimFormer++, which uses Selective Mixing blocks with windowed Mamba mixing in the first encoder stage and only residual channel MLPs in deeper stages. At the bottleneck, Augmented Mixing injects similarity-weighted sparse long-range context into the axial taps of a…
PDF arXiv
arXiv cs.CVOct 18
PCXR-Copilot: Device-aware pediatric chest radiographic analysis with segmentation, suppression, and edema-related cues
Xueyang Li, Alec Girten, Peng Zeng, Mingze Jiang, Shaoyang Huang +4
Patients admitted to pediatric ICU and cardiac ICU often require intensive perioperative support and frequent chest radiography to monitor support devices, extravascular lung water, and body-wall edema. Radiographic interpretation and analysis is often difficult due to superimposed anatomic and iatrogenic structures that obscure clinically relevant signals. While deep learning can provide decision support, translation to pediatric radiographs is still limited by high device burden and scarcity of pediatric datasets with clinically grounded device annotations. To address this gap, we first introduce PediaDeviceSeg-1K, a curated dataset of 1,000 anterior-posterior radiographs of pediatric CICU patients with clinically grounded, fine-grained annotations of common ICU devices. To our best knowledge, this is the first publicly available pediatric CICU CXR resource with clinically grounded device annotations for device-focused benchmarking. Building on PediaDeviceSeg-1K, we further present PCXR-Copilot, a device-aware pipeline that outputs lung and device masks, optional device-suppressed views, and edema-related visualization maps and scalar indices. The full pipeline has been…
PDF arXiv
arXiv cs.CVOct 18
Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
Antonio Scardace, Silvia Ingala, Sebastiano Battiato, Daniele Ravì
Spatiotemporal forecasting of 3D anatomical changes is a critical generative vision challenge for modeling Alzheimer's Disease (AD) progression. While brain MRI forecasting from single-modality data has advanced, other key complementary multimodal biomarkers remain underutilized. In this work, we propose the Multimodal-Conditioned Latent Diffusion Model (MC-LDM), a novel framework for patient-specific brain MRI synthesis. MC-LDM leverages multimodal biomarkers---MRI, tau-PET, Clinical Dementia Rating (CDR), and APOE4---during training, but relies only on baseline MRI, APOE4, and patient metadata at inference, reflecting realistic clinical availability. Our contributions are threefold: (1) a Latent Diffusion Model (LDM) fine-tuned via Low-Rank Adaptation (LoRA) for data-efficient learning; (2) a multimodal conditioning that integrates molecular and clinical signals to model heterogeneous disease trajectories; and (3) auxiliary predictors estimating unobserved multimodal biomarkers from MRI features and patient covariates to condition the diffusion model, removing the need for these assessments at inference. Trained on 1,355 visits from 852 subjects in the Alzheimer's Disease…
PDF arXiv
arXiv eess.IVOct 18
Reliability-Preserving Compression for Deployable Medical Image Segmentation
Sagar Kumar, Shailja Sharma, Tirthajit Baruah, Punit Rathore
Post-training compression of pretrained medical segmentation models commonly uses quantization, pruning, or distillation to reduce model size and computational cost. Existing medical segmentation quantization studies mainly evaluate Dice, model size, memory, and latency, but this is incomplete for safety-critical use because compressed models should also preserve uncertainty signals that indicate unreliable predictions. We study this problem in compressed MRI/CT segmentation and formalize why Dice can remain stable while uncertainty changes. Motivated by this failure mode, we propose score-guided aggregate-validated mixed precision (SGAV-MP), a post-training compression framework that selects layer precisions under reliability constraints. SGAV-MP ranks quantization actions with a compact benefit-risk score and accepts them only after full-model reliability validation on a calibration set. Across five 2D backbones and three 3D benchmarks, SGAV-MP preserves FP32-level Dice and high uncertainty-map agreement, achieving $3.59\times$--$7.8\times$ compression in 2D and $4.73\times$--$7.82\times$ in 3D. We also report backend-supported TensorRT deployment measurements separately to…
PDF arXiv
arXiv cs.CVOct 18
CoRe-WM: Conditioned Response-State World Modeling for Longitudinal Treatment-Response Prediction in Breast DCE-MRI
Lin Zhao, Bowen Chen, Qiaohui Gao, Shaowen Wan, Xiang Li +2
Pathologic complete response (pCR) after neoadjuvant chemotherapy is a key prognostic indicator in aggressive breast cancer, yet existing prediction approaches either rely on pretreatment imaging alone, missing the patient's evolving response, or optimize multi-visit classifiers under direct outcome supervision, coupling representation learning with the clinical endpoint. We formulate longitudinal pCR prediction as a world-modeling problem and propose Conditioned Response-state World Modeling (CoRe-WM), which learns a latent state-transition model that predicts the next imaging state from the observed sequence. The transition function is conditioned on patient clinical profile and assigned therapy through additive shift and feature-wise modulation, enabling patient- and treatment-specific dynamics. A Factorized Response State pathway further decouples treatment-response dynamics from imaging appearance by encoding the structural prior that response manifests primarily in lesion morphology. After pretraining, all parameters are frozen and a linear readout predicts pCR at each clinical decision point. We evaluate CoRe-WM on I-SPY2 under five-fold cross-validation and assess…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image381 papers49 this week
clinical reasoning · question answering · language models195 papers36 this week
patient history · clinical data · health records104 papers26 this week
eeg foundation models · electroencephalography eeg · eeg foundation144 papers18 this week
radiology report generation · medical vision language · vision language models106 papers18 this week
electrocardiogram ecg · electrocardiogram · ecgs137 papers14 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 15m ago

Image segmentation

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

161papers this week−23.7% 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 use longitudinal imaging and clinical records.
- From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoningraises · addresses
Gigapixel whole-slide image generation and segmentation1 paper raises it · 1 addresses it
Existing histopathology models operate at patch scale, but clinical supervision and diagnoses require whole-slide level coherence and fidelity.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses
Anatomically unsupported detail in generative super-resolution for CT1 paper raises it · 1 addresses it
Generative SR can produce visually plausible but anatomically incorrect vascular structures, leading to unreliable clinical interpretations.
- VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolutionraises · addresses
Mismatch between clinical use and evaluation benchmarks1 paper raises it · 1 addresses it
Benchmarks often fail to reflect real-world clinical queries, leading to poor generalization of model performance in deployment.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
Parameter-efficient fine-tuning for large-scale medical models1 paper raises it · 1 addresses it
Training or fully fine-tuning large models on limited neural or medical data is computationally expensive and often impractical.
- Scaling Parameter-Efficient Fine-Tuning for Brain Encodingraises · addresses

Named by an AI from these papers' abstracts.

Recent papers

All in Research →

arXiv cs.AIOct 18
From Clinical Risk Prediction to Operational Decisions: An Uncertainty-Aware Framework for ICU Staffing
Jiayan Yang, Ad Feelders, Arno Siebes
Advances in electronic health records (EHR) and large-scale critical care databases have enabled the development of machine learning models for the early detection of patient deterioration. While these models often achieve strong predictive performance for outcomes such as sepsis and mortality, most studies evaluate them purely on discrimination metrics. This leaves a critical gap: how to systematically translate probabilistic clinical risk estimates into downstream operational decisions under real-world constraints, such as shift rigidity and hiring delays. This study proposes an uncertainty-aware anticipatory staffing framework that bridges short-term patient risk prediction and staff planning without altering the upstream predictive model. Instead of relying on naive deterministic aggregation or fixed safety buffers, the framework explicitly models dynamic predictive uncertainty and evaluates extreme shortage exposure using Conditional Value-at-Risk (CVaR). These aggregated, risk-calibrated demand signals are then embedded into an hourly rolling-horizon scheduling policy to generate delay-constrained staffing decisions. Evaluated on realistic trajectories from the MIMIC-IV…
PDF arXiv
arXiv cs.CVOct 18
SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation
Yang Hong, Lei Zhang, Zhihong Liu, Jianqing Mo, Xujiong Ye
To determine where computation is needed in volumetric segmentation networks, we analyze block-level redundancy and stage-wise token-mixer contribution. Centered Kernel Alignment reveals high input-output representational similarity within blocks of representative convolutional, hybrid, and Transformer-style segmentation networks. Similarity-guided pruning reduces their parameter counts by 39-75% while decreasing mean Dice by less than 0.4 percentage points. To examine token mixers within blocks, we introduce Anatomical Relation Distortion, which quantifies how bypassing a mixer distorts feature relations between labeled anatomical positions. Applied to an unpruned candidate architecture, Centered Kernel Alignment indicates high representational redundancy in intermediate encoder mixers, whereas Anatomical Relation Distortion shows that they contribute little and the bottleneck mixer most. These findings motivate SlimFormer++, which uses Selective Mixing blocks with windowed Mamba mixing in the first encoder stage and only residual channel MLPs in deeper stages. At the bottleneck, Augmented Mixing injects similarity-weighted sparse long-range context into the axial taps of a…
PDF arXiv
arXiv cs.CVOct 18
PCXR-Copilot: Device-aware pediatric chest radiographic analysis with segmentation, suppression, and edema-related cues
Xueyang Li, Alec Girten, Peng Zeng, Mingze Jiang, Shaoyang Huang +4
Patients admitted to pediatric ICU and cardiac ICU often require intensive perioperative support and frequent chest radiography to monitor support devices, extravascular lung water, and body-wall edema. Radiographic interpretation and analysis is often difficult due to superimposed anatomic and iatrogenic structures that obscure clinically relevant signals. While deep learning can provide decision support, translation to pediatric radiographs is still limited by high device burden and scarcity of pediatric datasets with clinically grounded device annotations. To address this gap, we first introduce PediaDeviceSeg-1K, a curated dataset of 1,000 anterior-posterior radiographs of pediatric CICU patients with clinically grounded, fine-grained annotations of common ICU devices. To our best knowledge, this is the first publicly available pediatric CICU CXR resource with clinically grounded device annotations for device-focused benchmarking. Building on PediaDeviceSeg-1K, we further present PCXR-Copilot, a device-aware pipeline that outputs lung and device masks, optional device-suppressed views, and edema-related visualization maps and scalar indices. The full pipeline has been…
PDF arXiv
arXiv cs.CVOct 18
Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
Antonio Scardace, Silvia Ingala, Sebastiano Battiato, Daniele Ravì
Spatiotemporal forecasting of 3D anatomical changes is a critical generative vision challenge for modeling Alzheimer's Disease (AD) progression. While brain MRI forecasting from single-modality data has advanced, other key complementary multimodal biomarkers remain underutilized. In this work, we propose the Multimodal-Conditioned Latent Diffusion Model (MC-LDM), a novel framework for patient-specific brain MRI synthesis. MC-LDM leverages multimodal biomarkers---MRI, tau-PET, Clinical Dementia Rating (CDR), and APOE4---during training, but relies only on baseline MRI, APOE4, and patient metadata at inference, reflecting realistic clinical availability. Our contributions are threefold: (1) a Latent Diffusion Model (LDM) fine-tuned via Low-Rank Adaptation (LoRA) for data-efficient learning; (2) a multimodal conditioning that integrates molecular and clinical signals to model heterogeneous disease trajectories; and (3) auxiliary predictors estimating unobserved multimodal biomarkers from MRI features and patient covariates to condition the diffusion model, removing the need for these assessments at inference. Trained on 1,355 visits from 852 subjects in the Alzheimer's Disease…
PDF arXiv
arXiv eess.IVOct 18
Reliability-Preserving Compression for Deployable Medical Image Segmentation
Sagar Kumar, Shailja Sharma, Tirthajit Baruah, Punit Rathore
Post-training compression of pretrained medical segmentation models commonly uses quantization, pruning, or distillation to reduce model size and computational cost. Existing medical segmentation quantization studies mainly evaluate Dice, model size, memory, and latency, but this is incomplete for safety-critical use because compressed models should also preserve uncertainty signals that indicate unreliable predictions. We study this problem in compressed MRI/CT segmentation and formalize why Dice can remain stable while uncertainty changes. Motivated by this failure mode, we propose score-guided aggregate-validated mixed precision (SGAV-MP), a post-training compression framework that selects layer precisions under reliability constraints. SGAV-MP ranks quantization actions with a compact benefit-risk score and accepts them only after full-model reliability validation on a calibration set. Across five 2D backbones and three 3D benchmarks, SGAV-MP preserves FP32-level Dice and high uncertainty-map agreement, achieving $3.59\times$--$7.8\times$ compression in 2D and $4.73\times$--$7.82\times$ in 3D. We also report backend-supported TensorRT deployment measurements separately to…
PDF arXiv
arXiv cs.CVOct 18
CoRe-WM: Conditioned Response-State World Modeling for Longitudinal Treatment-Response Prediction in Breast DCE-MRI
Lin Zhao, Bowen Chen, Qiaohui Gao, Shaowen Wan, Xiang Li +2
Pathologic complete response (pCR) after neoadjuvant chemotherapy is a key prognostic indicator in aggressive breast cancer, yet existing prediction approaches either rely on pretreatment imaging alone, missing the patient's evolving response, or optimize multi-visit classifiers under direct outcome supervision, coupling representation learning with the clinical endpoint. We formulate longitudinal pCR prediction as a world-modeling problem and propose Conditioned Response-state World Modeling (CoRe-WM), which learns a latent state-transition model that predicts the next imaging state from the observed sequence. The transition function is conditioned on patient clinical profile and assigned therapy through additive shift and feature-wise modulation, enabling patient- and treatment-specific dynamics. A Factorized Response State pathway further decouples treatment-response dynamics from imaging appearance by encoding the structural prior that response manifests primarily in lesion morphology. After pretraining, all parameters are frozen and a linear readout predicts pCR at each clinical decision point. We evaluate CoRe-WM on I-SPY2 under five-fold cross-validation and assess…
PDF arXiv