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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 papers56 this week
clinical reasoning · question answering · language models202 papers36 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

Topics in this field

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arXiv cs.AIOct 13
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making
Jing Liu, Yongxing Qi, Ping Yao, Ting Wu
What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the public TCMEval-SDT benchmark for Traditional Chinese Medicine syndrome differentiation (200 cases), the binding constraint is output discipline and reasoning structure, not model capability alone: two years of model scaling move zero-shot scores by only -0.03 to +0.08, while output-discipline failures (non-terminating reasoning, format non-compliance, large run-to-run spread) persist even in frontier models. We test this with a model-agnostic agent framework formalizing decision making as a five-stage closed loop, enforced by a candidate x evidence matrix falsification protocol: every candidate is judged against every evidence item, at least half of the candidates must be falsified before a conclusion is licensed, and survivors are emitted in a machine-verifiable single-line format. Across five base models, the effect is sign-conditional on zero-shot output discipline: it rescues models whose discipline fails (DeepSeek-V4-Flash: +0.267/+0.271, entirely termination rescue), reduces a high-variance model's run-to-run standard deviation…
PDF arXiv
arXiv cs.CLOct 13
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts
Boya Zhang, Yuanchao Ma, Jiageng Wu, Krittaphas Chaisutyakorn, Rodrigo Gameiro +12
Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91-1.00 on pain relevance and pain…
PDF arXiv
arXiv cs.CVOct 13
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection
Rashi Niyas P, Ritika Jain, Hitika Tiwari
Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Existing methods either rely on hand-crafted features with shallow classifiers, which limits learning from raw signals, or train deep networks on full datasets that require re-sampling to counter class skew and demand substantial data and compute. To address this, we propose a novel Fall detection framework via Normalized-gradient and Embedding matched Synthetic Time-frequency condensation, dubbed FALLNEST, a data-centric approach that distills large, imbalanced IMU datasets into tiny, class-balanced synthetic training sets. Building on dual-domain time-series condensation (CondTSC), FALLNEST introduces a fall-specific 7-channel input with accelerometer vector magnitude, class-balanced K-Means initialization, and normalized gradient matching with gradient clipping, combined with MMD-based embedding alignment and multi-view Fourier-domain augmentation, so that class balance arises from the condensed set itself rather than from re-sampling. We evaluate on SisFall, KFall, and MobiFall using strict subject-wise splits and naturally…
PDF arXiv
arXiv cs.CVOct 13
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li +4
Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunistic screening because of its low cost and favorable safety profile. However, downstream tasks such as tumor detection and fine-grained disease diagnosis remain challenging due to NCCT's inherently low tissue contrast, lack of hemodynamic enhancement, and complex anatomical background. These limitations make subtle, heterogeneous, and multifocal hepatic lesions difficult to identify. Although large-scale self-supervised pretraining can learn robust representations from unlabeled NCCT scans, conventional voxel-wise reconstruction does not fully exploit the complementary information available in NCCT. Under weak NCCT contrast, low-level intensity patterns become less informative for pathology recognition, limiting the capture of lesion-specific discriminative features. In this paper, we propose the Liver Frequency-Spatial Unified Self-Supervised Pretraining (LiFUSE). The frequency branch applies random band-stop masking and spectrum reconstruction to encourage the model to capture subtle differences between lesions and surrounding…
PDF arXiv
arXiv cs.CVOct 13
Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation
Huiyi Ren, Ziming Fang, Jiaming Cai, Rui Xu, Haojie Li
Dual-student learning enriches predictive diversity in SSMIS by introducing a strongly augmented view alongside a weakly augmented view. Insufficient strong augmentation may homogenize the students, whereas excessive augmentation can corrupt anatomical evidence and destabilize collaboration. This challenge echoes feedback-regulated human instruction, where learners receive differentiated tasks and the task difficulty is continually adjusted according to their evolving responses. Inspired by these, we propose Human-Inspired Asymmetric Collaboration (HAC), a closed-loop dual-student framework that maintains weak--strong learning through adaptive difficulty calibration and geometry preservation. Specifically, Reinforcement-guided Difficulty Calibration (RDC) formulates strong-augmentation control as a window-level reinforcement learning process, using augmentation history, realized perturbations, and training responses to dynamically adjust the augmentation magnitude, operation number, and magnitude range. Meanwhile, the Gramian Geometry Preservation Constraint (G2PC) preserves the early-stage three-branch geometry through global volume and pairwise area constraints, preventing…
PDF arXiv
arXiv cs.CVOct 13
dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions
Xi Zhu, Yijie Li, Xiaofan Wang, Wei Zhang, Huafu Chen +2
Diffusion MRI (dMRI) probes water diffusion within the brain, providing unique information about tissue microstructure and white matter connectivity. High-quality dMRI enables advanced and accurate analyses but demands dense acquisitions, resulting in long scan times and hardware constraints that are often infeasible. Further, dMRI requires dedicated computational methods to support diverse analysis tasks (e.g., parcellation, registration, and synthesis) for both neuroscientific and clinical applications. Here, we present dMRI-F, a foundation model that unifies dMRI data enhancement and downstream tasks across diverse acquisitions and populations, while requiring only minimal acquisition input. dMRI-F is pre-trained with a q-space masked reconstruction task: predicting densely sampled multi-shell data from only six volumes. It learns generalizable latent feature representations of dMRI data, which serve as a core foundation for adapting to diverse downstream tasks, including brain parcellation, brain registration, and cross-modal synthesis. Across nine independently acquired datasets, we demonstrate that dMRI-F outperforms state-of-the-art methods with robust generalization…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image395 papers56 this week
clinical reasoning · question answering · language models202 papers36 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

Fields · AI · updated 38m ago

Image segmentation

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

181papers this week−13.8% 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.

Recent papers

All in Research →

arXiv cs.AIOct 13
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making
Jing Liu, Yongxing Qi, Ping Yao, Ting Wu
What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the public TCMEval-SDT benchmark for Traditional Chinese Medicine syndrome differentiation (200 cases), the binding constraint is output discipline and reasoning structure, not model capability alone: two years of model scaling move zero-shot scores by only -0.03 to +0.08, while output-discipline failures (non-terminating reasoning, format non-compliance, large run-to-run spread) persist even in frontier models. We test this with a model-agnostic agent framework formalizing decision making as a five-stage closed loop, enforced by a candidate x evidence matrix falsification protocol: every candidate is judged against every evidence item, at least half of the candidates must be falsified before a conclusion is licensed, and survivors are emitted in a machine-verifiable single-line format. Across five base models, the effect is sign-conditional on zero-shot output discipline: it rescues models whose discipline fails (DeepSeek-V4-Flash: +0.267/+0.271, entirely termination rescue), reduces a high-variance model's run-to-run standard deviation…
PDF arXiv
arXiv cs.CLOct 13
PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts
Boya Zhang, Yuanchao Ma, Jiageng Wu, Krittaphas Chaisutyakorn, Rodrigo Gameiro +12
Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91-1.00 on pain relevance and pain…
PDF arXiv
arXiv cs.CVOct 13
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection
Rashi Niyas P, Ritika Jain, Hitika Tiwari
Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Existing methods either rely on hand-crafted features with shallow classifiers, which limits learning from raw signals, or train deep networks on full datasets that require re-sampling to counter class skew and demand substantial data and compute. To address this, we propose a novel Fall detection framework via Normalized-gradient and Embedding matched Synthetic Time-frequency condensation, dubbed FALLNEST, a data-centric approach that distills large, imbalanced IMU datasets into tiny, class-balanced synthetic training sets. Building on dual-domain time-series condensation (CondTSC), FALLNEST introduces a fall-specific 7-channel input with accelerometer vector magnitude, class-balanced K-Means initialization, and normalized gradient matching with gradient clipping, combined with MMD-based embedding alignment and multi-view Fourier-domain augmentation, so that class balance arises from the condensed set itself rather than from re-sampling. We evaluate on SisFall, KFall, and MobiFall using strict subject-wise splits and naturally…
PDF arXiv
arXiv cs.CVOct 13
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li +4
Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunistic screening because of its low cost and favorable safety profile. However, downstream tasks such as tumor detection and fine-grained disease diagnosis remain challenging due to NCCT's inherently low tissue contrast, lack of hemodynamic enhancement, and complex anatomical background. These limitations make subtle, heterogeneous, and multifocal hepatic lesions difficult to identify. Although large-scale self-supervised pretraining can learn robust representations from unlabeled NCCT scans, conventional voxel-wise reconstruction does not fully exploit the complementary information available in NCCT. Under weak NCCT contrast, low-level intensity patterns become less informative for pathology recognition, limiting the capture of lesion-specific discriminative features. In this paper, we propose the Liver Frequency-Spatial Unified Self-Supervised Pretraining (LiFUSE). The frequency branch applies random band-stop masking and spectrum reconstruction to encourage the model to capture subtle differences between lesions and surrounding…
PDF arXiv
arXiv cs.CVOct 13
Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation
Huiyi Ren, Ziming Fang, Jiaming Cai, Rui Xu, Haojie Li
Dual-student learning enriches predictive diversity in SSMIS by introducing a strongly augmented view alongside a weakly augmented view. Insufficient strong augmentation may homogenize the students, whereas excessive augmentation can corrupt anatomical evidence and destabilize collaboration. This challenge echoes feedback-regulated human instruction, where learners receive differentiated tasks and the task difficulty is continually adjusted according to their evolving responses. Inspired by these, we propose Human-Inspired Asymmetric Collaboration (HAC), a closed-loop dual-student framework that maintains weak--strong learning through adaptive difficulty calibration and geometry preservation. Specifically, Reinforcement-guided Difficulty Calibration (RDC) formulates strong-augmentation control as a window-level reinforcement learning process, using augmentation history, realized perturbations, and training responses to dynamically adjust the augmentation magnitude, operation number, and magnitude range. Meanwhile, the Gramian Geometry Preservation Constraint (G2PC) preserves the early-stage three-branch geometry through global volume and pairwise area constraints, preventing…
PDF arXiv
arXiv cs.CVOct 13
dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions
Xi Zhu, Yijie Li, Xiaofan Wang, Wei Zhang, Huafu Chen +2
Diffusion MRI (dMRI) probes water diffusion within the brain, providing unique information about tissue microstructure and white matter connectivity. High-quality dMRI enables advanced and accurate analyses but demands dense acquisitions, resulting in long scan times and hardware constraints that are often infeasible. Further, dMRI requires dedicated computational methods to support diverse analysis tasks (e.g., parcellation, registration, and synthesis) for both neuroscientific and clinical applications. Here, we present dMRI-F, a foundation model that unifies dMRI data enhancement and downstream tasks across diverse acquisitions and populations, while requiring only minimal acquisition input. dMRI-F is pre-trained with a q-space masked reconstruction task: predicting densely sampled multi-shell data from only six volumes. It learns generalizable latent feature representations of dMRI data, which serve as a core foundation for adapting to diverse downstream tasks, including brain parcellation, brain registration, and cross-modal synthesis. Across nine independently acquired datasets, we demonstrate that dMRI-F outperforms state-of-the-art methods with robust generalization…
PDF arXiv