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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 papers60 this week
clinical reasoning · question answering · language models202 papers37 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers25 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers24 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers15 this week

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arXiv cs.AIOct 15
Auditing Escalation Reliability in Healthcare LLM Agents: A Perturbation-Controlled Benchmark of Workflow-State Failures
Zihao Zheng, Junyi Yao, Baichuan Li, Jiayu Long
Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static inputs. We instantiate an episode-level evaluation protocol as 400 synthetic healthcare workflow episodes: 40 hand-authored semantic seeds across four task classes, two surface parameterizations, and five controlled variants, scored on ANSWER/ASK CLARIFICATION/ABSTAIN/ESCALATE under a pre-declared 5:1 missed-versus-unnecessary escalation cost. Reference actions are single-author study-policy labels, not clinician-adjudicated ground truth. Across five open-weight configurations (1.7B--9.7B), clean decision accuracy reaches 0.97, yet uncertainty-prompted models miss 32--97% of required escalations. The stronger 9.7B configuration narrows but does not remove the gap, falling from 0.97 clean accuracy to 0.78 under conflict. Verbalized confidence is inconsistent (ECE 0.10--0.54; failure AUROC 0.48--0.77), and a development-tuned confidence-only policy yields no utility gain. A transparent workflow-aware controller improves 5:1 utility for every model and reduces missed escalations to 0.12--0.24, at specificity 0.83--0.93. However, a…
PDF arXiv
arXiv cs.HCOct 15
What do AI chatbots do after users decline therapeutic redirection for suicide thoughts?
Subigya Nepal, Ruth Heo, Emma M Parrish, Colin A Depp
Safety evaluations of artificial intelligence (AI) chatbots focus on therapeutic redirection, statements urging users who disclose suicide thoughts to seek help from crisis lines or other human sources. How chatbots respond when users refuse that help is unclear. This cross-sectional audit ran 2,550 scripted multi-turn conversations in September 2026. Each conversation opened with a disclosure of suicide thoughts at 3 levels of severity, answered every referral with up to 10 standardized refusals and then changed the subject. All 2,550 conversations included a referral, 2,542 (99.7%) in the first reply. However, mean persistence ranged from 0.01 to 9.94 refusals across chatbots. Newer versions of all 5 chatbots tested in 2 versions persisted less than older versions (each P < .05). Compared with passive ideation, a disclosure of intent was associated with 1.26 more refusals sustained (95% CI, 1.06-1.46) and higher odds of renewed referral after the topic change (odds ratio, 10.07; 95% CI, 7.43-13.66). Chatbots that persisted less in referral more often presented themselves as the user's support (Spearman rho = -0.80; P < .001). Of 8,818 refusal-phase replies urging outside help,…
PDF arXiv
arXiv eess.IVOct 15
Really Need Recursion? Revisiting One-pass Registration with Multi-level Multi-space Contextual Guidance
Yue Lin, Shenghai Wu, Mingyuan Meng
Deformable image registration is a fundamental challenge in medical image analysis. Recent deep learning-based methods typically adopt either one-pass architectures that complete registration in a single network iteration, or recursive architectures that refine registration through multiple passes. While recursive methods often improve accuracy, they incur substantial computational overhead and slower inference speed. In this paper, we challenge the necessity of recursion for high-performance deformable registration and propose M2CG, a one-pass registration network with Multi-level Multi-space Contextual Guidance. It performs multi-step progressive pyramid registration within a single iteration, guided at each step by contextual cues from three levels (previous, current, and subsequent steps) and four spaces (image space, image feature space, segmentation space, and segmentation feature space). Our M2CG supports both unsupervised (without any labels) and semi-supervised (with segmentation labels for partial training data) registration, and has achieved state-of-the-art performance across two well-benchmarked inter-/intra-patient medical registration tasks with eight public…
PDF arXiv
arXiv cs.LGOct 15
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble
Abdul Raheem Shahzad, Jinxin Chen, Ming Zhang, Juan Deng
Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous, and contain pathway-dependent variables prone to outcome leakage. We aimed to construct a curated single-centre IVF/ICSI dataset and develop a prediction model suited to small tabular clinical data. \textit{Materials and Methods:} We assembled 616 fresh embryo-transfer cycles (251 live birth, 365 non-live birth) with clinical, laboratory, and embryological predictors, and embryo morphology encoded with a novel differential weighting of the inner cell mass and trophectoderm. After KNN imputation, robust scaling, and selection of 50 features, we developed a Hybrid Attention Network (HAN), a learnable feature-attention gate followed by a compact multilayer perceptron, and a HAN + logistic-regression (LR) ensemble with a validation-tuned weight. Models were benchmarked against classical machine learning, TabPFN, and text-serialized language models on a held-out test set; AUC-ROC was the primary metric. \textit{Results:} The HAN + LR ensemble achieved the highest test AUC-ROC (0.710; accuracy 0.710, F1 0.630), ahead of the HAN…
PDF arXiv
arXiv cs.CLOct 15
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews
Zhaoqian Xue, Shaoze Zhou, Xuexin Li, Junting Mu, Haozheng Liu +8
Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tools based on large language models (LLMs) can search and summarize the literature but do not record which studies are excluded or where extracted values come from. We develop a conversational multi-agent system called ReviewPilot that automates the PRISMA workflow from paper screening to information extraction. Researchers can tailor criteria and schemas through dialogue, and a project memory keeps their configurations and revisions. Each screening decision comes with its reason and each extracted value with its source. On epidemiological expert reviews, ReviewPilot outperforms a single-prompt LLM and AgentSLR across three LLMs, with screening macro F1 up to 0.776 and extraction F1 up to 0.788. Case studies in biomedicine and human-computer interaction show close agreement with human annotators, suggesting that ReviewPilot can transfer beyond epidemiology. Code is available at https://github.com/ZhaoqianXue/ReviewPilot.
PDF arXiv
arXiv eess.SPOct 14
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora
Blessing Isoyiza Adeika, Onyema Osuagwu
Objective. A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical. We tested whether imagined-speech content recorded with scalp electroencephalography (EEG) transfers across people, and examined why accuracy within a single person remains low. Approach. We analyzed three public corpora, ds003626, KaraOne and Track 3 of the 2020 International Brain-Computer Interface Competition, under a protocol that enforces split integrity by assertion and reports exact binomial thresholds, group tests and bootstrap confidence intervals. We trained decoders on each participant and tested them on every other, compared representational geometry across participants without a classifier, applied subject-level alignment, and tested five explanations for the within-subject ceiling. Main results. Across 482 ordered pairs of participants, no pair survived correction for multiple comparisons; mean pair-transfer accuracy lay within 0.006 of chance on every corpus, while every within-subject reference lay above it. Imagery against pre-stimulus rest generalized at about 0.61 after artifact and elapsed-time controls, but its decoder…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image395 papers60 this week
clinical reasoning · question answering · language models202 papers37 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers25 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers24 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers15 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

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.

Recent papers

All in Research →

arXiv cs.AIOct 15
Auditing Escalation Reliability in Healthcare LLM Agents: A Perturbation-Controlled Benchmark of Workflow-State Failures
Zihao Zheng, Junyi Yao, Baichuan Li, Jiayu Long
Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static inputs. We instantiate an episode-level evaluation protocol as 400 synthetic healthcare workflow episodes: 40 hand-authored semantic seeds across four task classes, two surface parameterizations, and five controlled variants, scored on ANSWER/ASK CLARIFICATION/ABSTAIN/ESCALATE under a pre-declared 5:1 missed-versus-unnecessary escalation cost. Reference actions are single-author study-policy labels, not clinician-adjudicated ground truth. Across five open-weight configurations (1.7B--9.7B), clean decision accuracy reaches 0.97, yet uncertainty-prompted models miss 32--97% of required escalations. The stronger 9.7B configuration narrows but does not remove the gap, falling from 0.97 clean accuracy to 0.78 under conflict. Verbalized confidence is inconsistent (ECE 0.10--0.54; failure AUROC 0.48--0.77), and a development-tuned confidence-only policy yields no utility gain. A transparent workflow-aware controller improves 5:1 utility for every model and reduces missed escalations to 0.12--0.24, at specificity 0.83--0.93. However, a…
PDF arXiv
arXiv cs.HCOct 15
What do AI chatbots do after users decline therapeutic redirection for suicide thoughts?
Subigya Nepal, Ruth Heo, Emma M Parrish, Colin A Depp
Safety evaluations of artificial intelligence (AI) chatbots focus on therapeutic redirection, statements urging users who disclose suicide thoughts to seek help from crisis lines or other human sources. How chatbots respond when users refuse that help is unclear. This cross-sectional audit ran 2,550 scripted multi-turn conversations in September 2026. Each conversation opened with a disclosure of suicide thoughts at 3 levels of severity, answered every referral with up to 10 standardized refusals and then changed the subject. All 2,550 conversations included a referral, 2,542 (99.7%) in the first reply. However, mean persistence ranged from 0.01 to 9.94 refusals across chatbots. Newer versions of all 5 chatbots tested in 2 versions persisted less than older versions (each P < .05). Compared with passive ideation, a disclosure of intent was associated with 1.26 more refusals sustained (95% CI, 1.06-1.46) and higher odds of renewed referral after the topic change (odds ratio, 10.07; 95% CI, 7.43-13.66). Chatbots that persisted less in referral more often presented themselves as the user's support (Spearman rho = -0.80; P < .001). Of 8,818 refusal-phase replies urging outside help,…
PDF arXiv
arXiv eess.IVOct 15
Really Need Recursion? Revisiting One-pass Registration with Multi-level Multi-space Contextual Guidance
Yue Lin, Shenghai Wu, Mingyuan Meng
Deformable image registration is a fundamental challenge in medical image analysis. Recent deep learning-based methods typically adopt either one-pass architectures that complete registration in a single network iteration, or recursive architectures that refine registration through multiple passes. While recursive methods often improve accuracy, they incur substantial computational overhead and slower inference speed. In this paper, we challenge the necessity of recursion for high-performance deformable registration and propose M2CG, a one-pass registration network with Multi-level Multi-space Contextual Guidance. It performs multi-step progressive pyramid registration within a single iteration, guided at each step by contextual cues from three levels (previous, current, and subsequent steps) and four spaces (image space, image feature space, segmentation space, and segmentation feature space). Our M2CG supports both unsupervised (without any labels) and semi-supervised (with segmentation labels for partial training data) registration, and has achieved state-of-the-art performance across two well-benchmarked inter-/intra-patient medical registration tasks with eight public…
PDF arXiv
arXiv cs.LGOct 15
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble
Abdul Raheem Shahzad, Jinxin Chen, Ming Zhang, Juan Deng
Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous, and contain pathway-dependent variables prone to outcome leakage. We aimed to construct a curated single-centre IVF/ICSI dataset and develop a prediction model suited to small tabular clinical data. \textit{Materials and Methods:} We assembled 616 fresh embryo-transfer cycles (251 live birth, 365 non-live birth) with clinical, laboratory, and embryological predictors, and embryo morphology encoded with a novel differential weighting of the inner cell mass and trophectoderm. After KNN imputation, robust scaling, and selection of 50 features, we developed a Hybrid Attention Network (HAN), a learnable feature-attention gate followed by a compact multilayer perceptron, and a HAN + logistic-regression (LR) ensemble with a validation-tuned weight. Models were benchmarked against classical machine learning, TabPFN, and text-serialized language models on a held-out test set; AUC-ROC was the primary metric. \textit{Results:} The HAN + LR ensemble achieved the highest test AUC-ROC (0.710; accuracy 0.710, F1 0.630), ahead of the HAN…
PDF arXiv
arXiv cs.CLOct 15
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews
Zhaoqian Xue, Shaoze Zhou, Xuexin Li, Junting Mu, Haozheng Liu +8
Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tools based on large language models (LLMs) can search and summarize the literature but do not record which studies are excluded or where extracted values come from. We develop a conversational multi-agent system called ReviewPilot that automates the PRISMA workflow from paper screening to information extraction. Researchers can tailor criteria and schemas through dialogue, and a project memory keeps their configurations and revisions. Each screening decision comes with its reason and each extracted value with its source. On epidemiological expert reviews, ReviewPilot outperforms a single-prompt LLM and AgentSLR across three LLMs, with screening macro F1 up to 0.776 and extraction F1 up to 0.788. Case studies in biomedicine and human-computer interaction show close agreement with human annotators, suggesting that ReviewPilot can transfer beyond epidemiology. Code is available at https://github.com/ZhaoqianXue/ReviewPilot.
PDF arXiv
arXiv eess.SPOct 14
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora
Blessing Isoyiza Adeika, Onyema Osuagwu
Objective. A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical. We tested whether imagined-speech content recorded with scalp electroencephalography (EEG) transfers across people, and examined why accuracy within a single person remains low. Approach. We analyzed three public corpora, ds003626, KaraOne and Track 3 of the 2020 International Brain-Computer Interface Competition, under a protocol that enforces split integrity by assertion and reports exact binomial thresholds, group tests and bootstrap confidence intervals. We trained decoders on each participant and tested them on every other, compared representational geometry across participants without a classifier, applied subject-level alignment, and tested five explanations for the within-subject ceiling. Main results. Across 482 ordered pairs of participants, no pair survived correction for multiple comparisons; mean pair-transfer accuracy lay within 0.006 of chance on every corpus, while every within-subject reference lay above it. Imagery against pre-stimulus rest generalized at about 0.61 after artifact and elapsed-time controls, but its decoder…
PDF arXiv

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