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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 image403 papers62 this week
radiology report generation · medical vision language · vision language models127 papers38 this week
clinical reasoning · question answering · language models205 papers37 this week
patient history · clinical data · health records109 papers26 this week
eeg foundation models · electroencephalography eeg · eeg foundation155 papers24 this week
electrocardiogram ecg · electrocardiogram · ecgs145 papers17 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.CVOct 4
CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization
Yucheng Song, Jincan Wang, Haokang Ding, Zhiqiang Tian, Kangxu Fan +1
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): \textbf{Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness.} This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and…
PDF arXiv
arXiv eess.SPOct 4
Kapture: Capturing Cardiac Dynamics with Koopman-Governed Learning for Efficient Radar-Based Electrocardiogram Recovery
Tong Wu, Jing Peng, Ziqi Feng, Yuanyuan Zhang
Millimeter-wave (mmWave) radar enables unobtrusive, contactless electrocardiogram (ECG) reconstruction for cardiac monitoring. Time-frequency spectrograms preserve fine cardiac patterns but often require large backbones to separate ECG-relevant features from respiration, motion, multipath, and subject-dependent interference. We propose Kapture, a parameter-efficient Koopman-governed framework that projects radar hidden states into a low-dimensional observable space and identifies a regularized full linear evolution operator from adjacent observable states. The Koopman-predicted observables refine subsequent hidden states for ECG reconstruction. To suppress predictable interference dynamics, a temporal contrastive objective pulls neighboring states together and separates non-neighbors, while reconstruction supervision preserves task relevance. Using approximately 80 minutes of quasi-static radar-ECG recordings containing realistic noise from body movements and other sources, Kapture consistently improves reconstruction across matched backbone widths, with the largest gains under aggressive compression. The compact configuration approaches the full-width reference accuracy with…
PDF arXiv
arXiv cs.LGOct 4
Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer
Qingyang Zhang
Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and healthcare workflows differ substantially across institutions and countries. We introduce Asymmetric Supervised Contrastive Learning (Asymmetric SupCon), a task-specific pre-training objective motivated by the heterogeneity of negative clinical outcomes. The objective clusters patients sharing a target positive outcome without explicitly attracting negative trajectories toward one another. We pre-train temporal Transformer encoders on longitudinal records from 3.98 million patients in the Taiwanese National Health Insurance Research Database (NHIRD) and transfer them to two U.S. EHR datasets, MIMIC-IV and EHRSHOT. A hybrid semantic mapping pipeline combining direct mappings with embedding-based retrieval enables transfer across heterogeneous clinical vocabularies. On MIMIC-IV, NHIRD pre-training consistently improves over random initialization while substantially narrowing the performance gap to task-specific in-domain pre-training. On EHRSHOT, the transferred models show particularly strong few-shot performance for incident disease…
PDF arXiv
arXiv cs.CVOct 4
One Tile, Multiple Instances: Rethinking MIL for Sparse Diagnostic Evidence
Runsheng Liu, Cheng Jin, Hao Jiang, Hao Chen
In weakly supervised Whole Slide Image (WSI) classification, feature extractors typically compress each image tile into a single global embedding. Consequently, slide-level aggregators are restricted to this coarse tile scale, concealing fine-grained sub-tile evidence from the attention mechanism. We introduce DI-MIL, a framework that decouples encoding context from instance granularity through decomposed instances. By clustering dense spatial tokens from a frozen foundation model within each tile, DI-MIL converts a single tile into multiple independently weighted instance embeddings. As a training-free post-encoding module, DI-MIL integrates seamlessly into existing pipelines without requiring re-encoding or downstream architectural modifications. We evaluate DI-MIL on cytopathology, a challenging testbed where sparse diagnostic signals are easily diluted within standard tiles. Across four datasets, three frozen foundation models, and two attention-based aggregators, DI-MIL demonstrates consistent efficacy, improving 67 of 72 metric-level comparisons, with the largest mean gains reaching 3.64 points under cytopathology-specific backbones. In a broader comparison against seven…
PDF arXiv
arXiv cs.CVOct 3
MOXIE: Discovering Alternative Explanations for Biomedical Image Classifiers
Abiha Tahsin Chowdhury, Rahul Dubey
Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from one fixed image segmentation. This hides two important facts: a prediction can be supported by many different sets of image segments, and the segmentation itself shapes which explanations can be found. We introduce MOXIE (Multi-Objective eXplanation Imaging Engine), an evolutionary framework that searches for segment subsets that preserve the classifier's confidence while keeping as little of the image as possible. Instead of one explanation, MOXIE returns a Pareto front of alternative explanations that range from compact to highly faithful. We evaluate MOXIE with NSGA-II and four segmentation methods (SLIC, Felzenszwalb, Watershed and Voronoi) on BloodMNIST and HAM10000 datasets, using the same evaluation budget as LIME. Results show that MOXIE achieves a higher hypervolume than LIME on every image. LIME's explanations often appear convincing, yet the classifier's confidence collapses when only the highlighted segments are shown. MOXIE's fronts reveal how much of the image is needed to preserve the model's confidence and which contextual regions influence it. We also…
PDF arXiv
arXiv cs.CVOct 3
Active-DiNTS: Active Differentiable Network Topology Search
Gean Trindade Pereira, Thierry Urruty, Muriel Visani, André C. P. L. F. de Carvalho
Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is limited by two well-known costs, large annotation budgets and multi-GPU clusters. Thus, this paper introduces Active-DiNTS (Active Differentiable Network Topology Search), an approach that embeds pool-based Active Learning (AL) into a bi-level differentiable topology search to perform architecture discovery and label curation jointly. At each query round, unlabeled MRI volumes are ranked by one of three uncertainty signals (Entropy, Variance, or Standard Deviation), and only the top-ranked volumes are sent to an oracle for annotation. The new labels feed two interlocked stages. Network weights are updated in an outer loop, while the macro/micro topology of a U-Net-style backbone is refined in an inner loop. Three AL regimes (weights-only, topology-only, joint) expose the speed-accuracy trade-off. Evaluations on the Medical Segmentation Decathlon (MSD) Task01 BrainTumour benchmark showed that Active-DiNTS surpasses DiNTS, C2FNAS, and nnU-Net in Dice, with gains of about 10 percentage points on Edema and 5 points on Non-Enhancing core,…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image403 papers62 this week
radiology report generation · medical vision language · vision language models127 papers38 this week
clinical reasoning · question answering · language models205 papers37 this week
patient history · clinical data · health records109 papers26 this week
eeg foundation models · electroencephalography eeg · eeg foundation155 papers24 this week
electrocardiogram ecg · electrocardiogram · ecgs145 papers17 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CVOct 4
CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization
Yucheng Song, Jincan Wang, Haokang Ding, Zhiqiang Tian, Kangxu Fan +1
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): \textbf{Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness.} This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and…
PDF arXiv
arXiv eess.SPOct 4
Kapture: Capturing Cardiac Dynamics with Koopman-Governed Learning for Efficient Radar-Based Electrocardiogram Recovery
Tong Wu, Jing Peng, Ziqi Feng, Yuanyuan Zhang
Millimeter-wave (mmWave) radar enables unobtrusive, contactless electrocardiogram (ECG) reconstruction for cardiac monitoring. Time-frequency spectrograms preserve fine cardiac patterns but often require large backbones to separate ECG-relevant features from respiration, motion, multipath, and subject-dependent interference. We propose Kapture, a parameter-efficient Koopman-governed framework that projects radar hidden states into a low-dimensional observable space and identifies a regularized full linear evolution operator from adjacent observable states. The Koopman-predicted observables refine subsequent hidden states for ECG reconstruction. To suppress predictable interference dynamics, a temporal contrastive objective pulls neighboring states together and separates non-neighbors, while reconstruction supervision preserves task relevance. Using approximately 80 minutes of quasi-static radar-ECG recordings containing realistic noise from body movements and other sources, Kapture consistently improves reconstruction across matched backbone widths, with the largest gains under aggressive compression. The compact configuration approaches the full-width reference accuracy with…
PDF arXiv
arXiv cs.LGOct 4
Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer
Qingyang Zhang
Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and healthcare workflows differ substantially across institutions and countries. We introduce Asymmetric Supervised Contrastive Learning (Asymmetric SupCon), a task-specific pre-training objective motivated by the heterogeneity of negative clinical outcomes. The objective clusters patients sharing a target positive outcome without explicitly attracting negative trajectories toward one another. We pre-train temporal Transformer encoders on longitudinal records from 3.98 million patients in the Taiwanese National Health Insurance Research Database (NHIRD) and transfer them to two U.S. EHR datasets, MIMIC-IV and EHRSHOT. A hybrid semantic mapping pipeline combining direct mappings with embedding-based retrieval enables transfer across heterogeneous clinical vocabularies. On MIMIC-IV, NHIRD pre-training consistently improves over random initialization while substantially narrowing the performance gap to task-specific in-domain pre-training. On EHRSHOT, the transferred models show particularly strong few-shot performance for incident disease…
PDF arXiv
arXiv cs.CVOct 4
One Tile, Multiple Instances: Rethinking MIL for Sparse Diagnostic Evidence
Runsheng Liu, Cheng Jin, Hao Jiang, Hao Chen
In weakly supervised Whole Slide Image (WSI) classification, feature extractors typically compress each image tile into a single global embedding. Consequently, slide-level aggregators are restricted to this coarse tile scale, concealing fine-grained sub-tile evidence from the attention mechanism. We introduce DI-MIL, a framework that decouples encoding context from instance granularity through decomposed instances. By clustering dense spatial tokens from a frozen foundation model within each tile, DI-MIL converts a single tile into multiple independently weighted instance embeddings. As a training-free post-encoding module, DI-MIL integrates seamlessly into existing pipelines without requiring re-encoding or downstream architectural modifications. We evaluate DI-MIL on cytopathology, a challenging testbed where sparse diagnostic signals are easily diluted within standard tiles. Across four datasets, three frozen foundation models, and two attention-based aggregators, DI-MIL demonstrates consistent efficacy, improving 67 of 72 metric-level comparisons, with the largest mean gains reaching 3.64 points under cytopathology-specific backbones. In a broader comparison against seven…
PDF arXiv
arXiv cs.CVOct 3
MOXIE: Discovering Alternative Explanations for Biomedical Image Classifiers
Abiha Tahsin Chowdhury, Rahul Dubey
Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from one fixed image segmentation. This hides two important facts: a prediction can be supported by many different sets of image segments, and the segmentation itself shapes which explanations can be found. We introduce MOXIE (Multi-Objective eXplanation Imaging Engine), an evolutionary framework that searches for segment subsets that preserve the classifier's confidence while keeping as little of the image as possible. Instead of one explanation, MOXIE returns a Pareto front of alternative explanations that range from compact to highly faithful. We evaluate MOXIE with NSGA-II and four segmentation methods (SLIC, Felzenszwalb, Watershed and Voronoi) on BloodMNIST and HAM10000 datasets, using the same evaluation budget as LIME. Results show that MOXIE achieves a higher hypervolume than LIME on every image. LIME's explanations often appear convincing, yet the classifier's confidence collapses when only the highlighted segments are shown. MOXIE's fronts reveal how much of the image is needed to preserve the model's confidence and which contextual regions influence it. We also…
PDF arXiv
arXiv cs.CVOct 3
Active-DiNTS: Active Differentiable Network Topology Search
Gean Trindade Pereira, Thierry Urruty, Muriel Visani, André C. P. L. F. de Carvalho
Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is limited by two well-known costs, large annotation budgets and multi-GPU clusters. Thus, this paper introduces Active-DiNTS (Active Differentiable Network Topology Search), an approach that embeds pool-based Active Learning (AL) into a bi-level differentiable topology search to perform architecture discovery and label curation jointly. At each query round, unlabeled MRI volumes are ranked by one of three uncertainty signals (Entropy, Variance, or Standard Deviation), and only the top-ranked volumes are sent to an oracle for annotation. The new labels feed two interlocked stages. Network weights are updated in an outer loop, while the macro/micro topology of a U-Net-style backbone is refined in an inner loop. Three AL regimes (weights-only, topology-only, joint) expose the speed-accuracy trade-off. Evaluations on the Medical Segmentation Decathlon (MSD) Task01 BrainTumour benchmark showed that Active-DiNTS surpasses DiNTS, C2FNAS, and nnU-Net in Dice, with gains of about 10 percentage points on Edema and 5 points on Non-Enhancing core,…
PDF arXiv

Activity · 12 weeks

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.

Fields · AI · updated 34m ago

Image segmentation

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

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