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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.

Open problems

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Topics in this field

image segmentation · volumetric · medical image395 papers54 this week
clinical reasoning · question answering · language models202 papers34 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers19 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers13 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.LGOct 5
Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs
Jiawen Du, Arshan Ali Khan, Chenhao Zhang, Zachary Plotkin, Li Shen +5
Clinical questions often depend on linking a patient's multimodal evidence to external biomedical knowledge, yet existing predictive systems rarely represent such links explicitly, so they can neither be traced to their evidence sources nor removed to measure their contributions. We present MM-KG (Multimodal Knowledge Graph), which represents heterogeneous, multimodal patient observations and biomedical concepts as separate layers in one typed graph, joined by explicit alignment edges. First, modality-specific harmonizers convert EHR text, imaging, genomic, and biospecimen data into typed observations mapped to UMLS concepts, which a route-prioritized aligner links to a biomedical knowledge graph. Query-conditioned retrieval then selects a compact subgraph for downstream use by a large language model or a graph neural network. We build MM-KGs for MIMIC-IV and ADNI, and evaluate them with a 2x2 design that separates patient evidence, biomedical knowledge, and their interaction. On questions that require both sources, neither source alone performs far above chance, whereas their combination yields a drug-controlled AUROC interaction of +0.194 on MIMIC and +0.299 on ADNI. On…
PDF arXiv
arXiv cs.CVOct 5
Cross-dataset harmonization for robust endoscopic image analysis
Romil Imtiaz, Dimitris K. Iakovidis
A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on images acquired from endoscopes that are different from those used to acquire the images of their training set. The main difference of the images originating from different endoscopes is their color distributions, which depend both on the image sensors and the light sources used. Although previous studies have highlighted this challenge, to the best of our knowledge it has not been previously explicitly tackled. This study focuses on this problem and proposes very simple but impactful method. It implements a reference-based image harmonization that reduces global appearance differences between endoscopic datasets. Specifically, it extracts global color statistics from a chosen reference dataset in the CIE-Lab color space and applies a statistical channel-wise transformation to map each target image toward the appearance of the images of the reference dataset. The method is evaluated in the context of polyp detection in both flexible colonoscopy and capsule endoscopy datasets using a dataset-level cross validation protocol. The…
PDF arXiv
arXiv cs.LGOct 5
Beyond the Model: The Critical Role of Data Filtering in Clinical Machine Learning
Noah Subedar, Colin Campbell, Wenjing Zhang, Dan Perri, Sarah Culgin +1
Machine learning (ML) studies using clinical data often rely on preprocessing and filtering pipelines before model development. The filtering decisions made in these pipelines can alter the dataset's statistical structure and may artificially reduce or increase the complexity of the prediction task. We argue that filtering choices should be treated as part of the scientific method rather than as a routine preprocessing step. We further discuss the need for explainable and transparent preprocessing pipelines that allow researchers to understand why specific filtering choices are made and how these choices affect the resulting data distribution and model performance. All of the source code for this work is available on GitHub.
PDF arXiv
arXiv q-bio.NCOct 5
COMPASS 2.0: psychometric representational similarity analysis distinguishes symptom structure from personal signal
Baihan Lin
Language models can score psychiatric questionnaires from speech, but agreement with self-report may reflect the questionnaire rather than the person. We introduce psychometric representational similarity analysis, a framework for comparing the structure of speech-derived scores, self-report, item wording and theory, and implement it alongside person-level construct scoring in COMPASS 2.0. We show how similarly worded items induce covariance without psychological signal. In pre-registered discovery and confirmation analyses of clinical interviews from 275 participants, language-derived symptom geometry resembled wording more than self-report, with no structure beyond wording detected by the registered tests. Geometric agreement with self-report survived assigning participants someone else's answers, whereas person-paired scores captured distress more than specific symptoms. Complementary analyses examined counselling quality and wording structure across 34 instruments and the Research Domain Criteria (RDoC) framework. These findings distinguish agreement about psychological structure from evidence that language-derived assessments track individual people.
PDF arXiv
arXiv cs.CVOct 5
Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable $T_{1ρ}$ and $T_2$ Quantification Without High-Resolution Morphological Images
Ahmed Tahseen Minhaz, Richard Lartey, Zhiyuan Zhang, Jeehun Kim, Kunio Nakamura +6
Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-consuming, costly 3D high-resolution Double Echo Steady-State (DESS) MRI scans. This study developed a multi-task conditional generative adversarial network (MT-cGAN) to simultaneously synthesize DESS-like images and segment tissues directly from qMRI echo images. This retrospective study evaluated 508 knee MRI volumes from 361 subjects (mean age: $40.4 \pm 12.2$ years; 179 female) across three cohorts. Ground truth segmentation masks were generated from DESS images using a pretrained model with manual correction, and $T_{1ρ}$ and $T_2$ maps were computed from magnetization-prepared angle-modulated partitioned $k$-space spoiled gradient echo snapshots (MAPSS) echo images. MT-cGAN was trained to jointly synthesize DESS-like images and segment cartilage and meniscus directly from echo images. Model performance was evaluated using Dice score for segmentation accuracy and coefficient of variation (CV) for $T_{1ρ}$ and $T_2$ quantification. MT-cGAN achieved the highest segmentation performance, mean Dice score 0.84 (range: 0.80--0.86)…
PDF arXiv
arXiv cs.CVOct 5
BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
Qizhen Lan, Mengchen Fan, Hang Zhang, Jingwei Duan, Moule Lin +3
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within volumetric anatomy, and translate this evidence into report-grounded assessments. Existing medical VQA and 3D imaging benchmarks capture important parts of this workflow, but often evaluate brain MRI through isolated images, static volumes, or ungrounded report-style answers, thereby obscuring failures in the evidence chain that support clinical validity. We introduce BrainTRACE, a report-grounded benchmark for evaluating whether vision-language models can trace the evidence structure required for longitudinal brain MRI interpretation. BrainTRACE contains 7,273 scored VQA instances derived from 1,778 longitudinal patients, 7,299 MRI studies, and approximately 29k co-registered 3D MRI sequence volumes. The benchmark is organized by five levels of clinical reasoning, from acquisition recognition to case-level synthesis, and by evidence demands covering longitudinal comparison, report-grounded references, multi-sequence integration, and volumetric spatial evidence. BrainTRACE supports rendered inputs…
PDF arXiv

Papers

Topics in this field

image segmentation · volumetric · medical image395 papers54 this week
clinical reasoning · question answering · language models202 papers34 this week
radiology report generation · medical vision language · vision language models114 papers25 this week
patient history · clinical data · health records105 papers23 this week
eeg foundation models · electroencephalography eeg · eeg foundation150 papers19 this week
electrocardiogram ecg · electrocardiogram · ecgs141 papers13 this week

Active contributors · 90 days

Activity · 12 weeks

Recent papers

All papers in Research

arXiv cs.LGOct 5
Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs
Jiawen Du, Arshan Ali Khan, Chenhao Zhang, Zachary Plotkin, Li Shen +5
Clinical questions often depend on linking a patient's multimodal evidence to external biomedical knowledge, yet existing predictive systems rarely represent such links explicitly, so they can neither be traced to their evidence sources nor removed to measure their contributions. We present MM-KG (Multimodal Knowledge Graph), which represents heterogeneous, multimodal patient observations and biomedical concepts as separate layers in one typed graph, joined by explicit alignment edges. First, modality-specific harmonizers convert EHR text, imaging, genomic, and biospecimen data into typed observations mapped to UMLS concepts, which a route-prioritized aligner links to a biomedical knowledge graph. Query-conditioned retrieval then selects a compact subgraph for downstream use by a large language model or a graph neural network. We build MM-KGs for MIMIC-IV and ADNI, and evaluate them with a 2x2 design that separates patient evidence, biomedical knowledge, and their interaction. On questions that require both sources, neither source alone performs far above chance, whereas their combination yields a drug-controlled AUROC interaction of +0.194 on MIMIC and +0.299 on ADNI. On…
PDF arXiv
arXiv cs.CVOct 5
Cross-dataset harmonization for robust endoscopic image analysis
Romil Imtiaz, Dimitris K. Iakovidis
A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on images acquired from endoscopes that are different from those used to acquire the images of their training set. The main difference of the images originating from different endoscopes is their color distributions, which depend both on the image sensors and the light sources used. Although previous studies have highlighted this challenge, to the best of our knowledge it has not been previously explicitly tackled. This study focuses on this problem and proposes very simple but impactful method. It implements a reference-based image harmonization that reduces global appearance differences between endoscopic datasets. Specifically, it extracts global color statistics from a chosen reference dataset in the CIE-Lab color space and applies a statistical channel-wise transformation to map each target image toward the appearance of the images of the reference dataset. The method is evaluated in the context of polyp detection in both flexible colonoscopy and capsule endoscopy datasets using a dataset-level cross validation protocol. The…
PDF arXiv
arXiv cs.LGOct 5
Beyond the Model: The Critical Role of Data Filtering in Clinical Machine Learning
Noah Subedar, Colin Campbell, Wenjing Zhang, Dan Perri, Sarah Culgin +1
Machine learning (ML) studies using clinical data often rely on preprocessing and filtering pipelines before model development. The filtering decisions made in these pipelines can alter the dataset's statistical structure and may artificially reduce or increase the complexity of the prediction task. We argue that filtering choices should be treated as part of the scientific method rather than as a routine preprocessing step. We further discuss the need for explainable and transparent preprocessing pipelines that allow researchers to understand why specific filtering choices are made and how these choices affect the resulting data distribution and model performance. All of the source code for this work is available on GitHub.
PDF arXiv
arXiv q-bio.NCOct 5
COMPASS 2.0: psychometric representational similarity analysis distinguishes symptom structure from personal signal
Baihan Lin
Language models can score psychiatric questionnaires from speech, but agreement with self-report may reflect the questionnaire rather than the person. We introduce psychometric representational similarity analysis, a framework for comparing the structure of speech-derived scores, self-report, item wording and theory, and implement it alongside person-level construct scoring in COMPASS 2.0. We show how similarly worded items induce covariance without psychological signal. In pre-registered discovery and confirmation analyses of clinical interviews from 275 participants, language-derived symptom geometry resembled wording more than self-report, with no structure beyond wording detected by the registered tests. Geometric agreement with self-report survived assigning participants someone else's answers, whereas person-paired scores captured distress more than specific symptoms. Complementary analyses examined counselling quality and wording structure across 34 instruments and the Research Domain Criteria (RDoC) framework. These findings distinguish agreement about psychological structure from evidence that language-derived assessments track individual people.
PDF arXiv
arXiv cs.CVOct 5
Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable $T_{1ρ}$ and $T_2$ Quantification Without High-Resolution Morphological Images
Ahmed Tahseen Minhaz, Richard Lartey, Zhiyuan Zhang, Jeehun Kim, Kunio Nakamura +6
Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-consuming, costly 3D high-resolution Double Echo Steady-State (DESS) MRI scans. This study developed a multi-task conditional generative adversarial network (MT-cGAN) to simultaneously synthesize DESS-like images and segment tissues directly from qMRI echo images. This retrospective study evaluated 508 knee MRI volumes from 361 subjects (mean age: $40.4 \pm 12.2$ years; 179 female) across three cohorts. Ground truth segmentation masks were generated from DESS images using a pretrained model with manual correction, and $T_{1ρ}$ and $T_2$ maps were computed from magnetization-prepared angle-modulated partitioned $k$-space spoiled gradient echo snapshots (MAPSS) echo images. MT-cGAN was trained to jointly synthesize DESS-like images and segment cartilage and meniscus directly from echo images. Model performance was evaluated using Dice score for segmentation accuracy and coefficient of variation (CV) for $T_{1ρ}$ and $T_2$ quantification. MT-cGAN achieved the highest segmentation performance, mean Dice score 0.84 (range: 0.80--0.86)…
PDF arXiv
arXiv cs.CVOct 5
BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
Qizhen Lan, Mengchen Fan, Hang Zhang, Jingwei Duan, Moule Lin +3
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within volumetric anatomy, and translate this evidence into report-grounded assessments. Existing medical VQA and 3D imaging benchmarks capture important parts of this workflow, but often evaluate brain MRI through isolated images, static volumes, or ungrounded report-style answers, thereby obscuring failures in the evidence chain that support clinical validity. We introduce BrainTRACE, a report-grounded benchmark for evaluating whether vision-language models can trace the evidence structure required for longitudinal brain MRI interpretation. BrainTRACE contains 7,273 scored VQA instances derived from 1,778 longitudinal patients, 7,299 MRI studies, and approximately 29k co-registered 3D MRI sequence volumes. The benchmark is organized by five levels of clinical reasoning, from acquisition recognition to case-level synthesis, and by evidence demands covering longitudinal comparison, report-grounded references, multi-sequence integration, and volumetric spatial evidence. BrainTRACE supports rendered inputs…
PDF arXiv

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 10m ago

Image segmentation

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

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