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arXiv cs.LGOct 23
U-NeXt: General-purpose event detection for neonatal EEG
Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin +2
We developed U-NeXt, a general framework for neonatal electroencephalography (EEG) event detection based on semantic segmentation. Its convolutional encoder-decoder directly segments individual channels using long input segments to preserve temporal context. A shared architecture and training framework were developed using low-voltage interval, artefact, and seizure datasets containing 51, 98, and 278 neonates, respectively, comprising over 11,000 hours of EEG and 82,000 annotated events. Performance was compared with task-specific ConvNeXt classifiers. For seizure detection, we also evaluated U-NeXt+, trained on an expanded dataset. Both U-NeXt variants and the ConvNeXt baseline were evaluated on two independent held-out seizure datasets, Helsinki and Cork, comprising 130 neonates, with recordings annotated by three experts. U-NeXt improved detection across all three event types, with particular gains in sensitivity for short events. U-NeXt+ achieved Matthews correlation coefficients of 0.862 and 0.891 on Helsinki and Cork, respectively, and improved hourly seizure burden correlations by 30-40% relative to ConvNeXt. U-NeXt+ met the criterion for expert-level performance on both…
PDF arXiv
arXiv cs.LGOct 23
Deep Learning-Based Prediction of HER2 Status from Whole-Brain MRI in Breast Cancer Brain Metastases
Sara Early, Chris Kang, Vibujithan Vigneshwaran, Kimberly Amador, Roger Y. Tsang +1
Purpose: Human epidermal growth factor receptor 2 (HER2) status is a key biomarker relevant for prognostication and treatment decision making in metastatic breast cancer. Thus, the aim of this work was to explore whether HER2 status can be predicted directly from whole-brain post-contrast T1-weighted (T1w) MRI scans in breast cancer brain metastases (BCBM) using deep learning, and whether providing explicit tumour segmentation information improves prediction. Methods: Using the publicly available BCBM-Radiogenomics dataset, we analysed 135 patients, including 70 HER2+ and 65 HER2- patients. Three approaches were implemented and evaluated: a conventional radiomics model, a simple fully convolutional network (SFCN) using T1w MRI scans alone, and an SFCN using T1w MRI scans with an expert-derived tumour mask as an additional input channel. Results: The radiomics model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.602 (95% confidence interval [CI]: 0.528--0.677) while the SFCN achieved a ROC-AUC of 0.667 (95% CI, 0.63--0.71) using T1w MRI scans alone, which increased to 0.692 (95% CI, 0.58--0.81) when an explicit tumour mask was additionally…
PDF arXiv
arXiv cs.CVOct 23
Cross-Architectural Feature Fusion for Coronary Artery Stenosis Detection in X-ray Angiography
Haobo Zhu, Mojtaba Lashgari, Jason Chai, Giovanni Luigi De Maria, Vicente Grau +1
Coronary artery disease remains a leading cause of mortality, with stenosis detection from invasive X-ray Coronary Angiography (XCA) critical for diagnosis. Manual interpretation is time-consuming, subjective, and variable, especially under low contrast and vessel overlap. Existing deep learning methods often rely solely on detection or segmentation, limiting performance. We propose a hybrid framework, Fusion-YOLOv11-L, combining a YOLOv11-L detector with a pretrained UNet++ via a channel attention gating module. This enables fusion of semantic and vessel-aware structural features, improving localisation of stenotic regions. The UNet++ is pretrained on ARCADE vessel segmentation dataset to provide anatomically informed representations. Experiments on the relabeled ARCADE (ARCADE*) dataset show improved performance over YOLOv11-L, achieving AP@50 of 0.585 and F1-score of 0.596. Cross-domain evaluation on a dataset from the Oxford Acute Myocardial Infarction (OxAMI) Study reveals performance degradation due to domain shift, mitigated using test-time adaptation and augmentation. Test-time augmentation improves AP@50 from 0.434 to 0.480, indicating better generalisability. These…
PDF arXiv
arXiv eess.SPOct 23
EMG Tokenization Preserves Movement Information at a Fraction of the Bitrate
Raul C. Sîmpetru, Amin Olamazadeh, Devon Rohlf, Vlad Cnejevici, Alessandro Del Vecchio
Wireless electromyography (EMG) interfaces must balance electrode coverage against transmission bandwidth. Here, we show that compact EMG tokens substantially reduce payload while retaining information for three-dimensional hand-pose decoding. We evaluate two causal tokenizers fitted without pose labels: an encoder pretrained to reconstruct EMG spectra and one based on SPAR, an engineered spectral-spatial representation of neighbouring electrodes. In personalized, within-session experiments with 320-channel EMG from nine participants, we compare tokenized and continuous features using matched decoder architectures and training procedures. Averaged across decoders, tokenization changed mean error by +0.01 mm for SPAR (95% confidence interval: -0.12 to 0.14) and +0.45 mm for the learned representation (0.10 to 0.82). Both increases met a 1 mm non-inferiority criterion. Stored token arrays reduced payloads 41-fold relative to raw EMG and 36-fold relative to continuous SPAR features. On sampled windows of the public emg2pose benchmark's test partition, absolute differences in mean error between learned tokens and continuous features were below 0.05 mm across 176 participants and in…
PDF arXiv
arXiv cs.CVOct 23
Hair Segmentation and Skin Characterization on Macroscopic Dermatological Images for Laser Treatment Parameterization
Alex Navarro Navarro, Marc Arriaga Hidalgo, Carlos Moreno Rubio, Maxime Talarmain, Noelia Vaquero Gallardo
Current light-based devices, such as laser hair removal, intense pulsed light (IPL), vascular laser therapy, and dermatologic phototherapy, are widely used in clinical dermatology but rely heavily on subjective manual parameterization or uncalibrated point-based colorimeters. To overcome these limitations, this work presents a unified system for hair segmentation and skin characterization in macroscopic dermatological images. Unlike most existing literature that relies on dermoscopic datasets captured under highly controlled conditions, our approach is specifically designed for macroscopic images that replicate real clinical laser treatments. The proposed pipeline integrates a custom-trained variant of the YOLOv9 deep learning architecture for instance segmentation and classical image processing within a single workflow to extract objective skin and hair parameters such as phototype, hair density, color, and thickness. A color calibration stage using spectrophotometric measurements ensures accurate feature estimation regardless of illumination conditions. Experimental results show competitive segmentation metrics (mAP@50 $\approx$ 0.80) on a custom dataset featuring short, shaved…
PDF arXiv
arXiv cs.CVOct 23
Ampe-DB: A Dataset for Human Activity and Interaction Analysis Using the Ghanaian Game Ampe
Ruth Senior Biney, Isaac Kojo Yedonu Aboah
Human activity recognition benchmarks underrepresent culturally situated forms of movement and interaction. We introduce Ampe-DB, a multimodal dataset based on Ampe, a traditional Ghanaian game involving coordinated jumping, leg movements, and two-person interaction. It contains 637 canonical clips from 15 participants, with RGB video, two-person pose representations, participant and pair metadata, four-class movement labels, and derived binary movement-relation labels. We evaluate four-class movement recognition and binary movement-relation classification using three protocols: a duplicate-safe fixed benchmark, four-fold pair-held-out evaluation, and participant-held-out diagnostics for participants with complete class coverage. Baselines comprise a frozen Kinetics-400-pretrained R(2+1)D-18 RGB encoder, a two-layer bidirectional GRU for pose sequences, and validation-selected RGB-pose late fusion. For four-class recognition, RGB macro-F1 falls from 0.955 on the fixed benchmark to 0.418 under pair-held-out evaluation and 0.384 under participant-held-out evaluation. Pose is more robust, achieving 0.957, 0.811, and 0.839 across the same protocols. Fusion reaches 0.976 on the fixed…
PDF arXiv

Papers

Active contributors · 90 days

Topics in this field

clinical reasoning · clinical decision · language models278 papers33 this week
brain tumor segmentation · medical image segmentation · brain mri141 papers17 this week
slide images wsis · pathology foundation models · images wsis116 papers13 this week
deformable image registration · medical image segmentation · 3d medical99 papers12 this week
radiology report generation · radiomics · radiology reports160 papers11 this week
clinical data · longitudinal electronic health · health records122 papers7 this week

Activity · 12 weeks

Open problems in Clinical reasoning

Lack of domain-specific benchmarks for clinical reasoning3 papers raise it · 2 address it
General benchmarks fail to capture critical domain requirements like safety, physiological appropriateness, and actionable quality, risking harm in real-world use.
Handling incidental or irrelevant information in clinical data1 paper raises it · 0 address it
LLMs may misattribute or use non-clinical asides or background noise in reasoning, leading to unsafe or incorrect outputs.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises
Temporal modeling of multimodal clinical trajectories1 paper raises it · 1 addresses it
Accurate risk prediction requires modeling irregular, heterogeneous clinical events over time, which current methods underaddress.
- TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Predictionraises · addresses
Learning from deployment experience without retraining1 paper raises it · 1 addresses it
Frozen models cannot adapt to new clinical evidence or guidelines, limiting their long-term utility in evolving medical practice.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses
Evidence-grounded reasoning in tool-augmented medical agents1 paper raises it · 2 address it
Agents often lack mechanisms to judge when auxiliary evidence is needed or to prevent invalid evidence from influencing reasoning.
- X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoningraises · addresses
- MMedAgent-Plus: Scaling Multimodal Medical Agents across Modalities, Tasks and Workflowsaddresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 19m ago

Clinical reasoning

Found by the topic model: clinical reasoning, radiology report generation, electroencephalography eeg.

120papers this week−45.0% vs last week

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817 papers this month

Recent papers

All papers in Research

arXiv cs.LGOct 23
U-NeXt: General-purpose event detection for neonatal EEG
Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin +2
We developed U-NeXt, a general framework for neonatal electroencephalography (EEG) event detection based on semantic segmentation. Its convolutional encoder-decoder directly segments individual channels using long input segments to preserve temporal context. A shared architecture and training framework were developed using low-voltage interval, artefact, and seizure datasets containing 51, 98, and 278 neonates, respectively, comprising over 11,000 hours of EEG and 82,000 annotated events. Performance was compared with task-specific ConvNeXt classifiers. For seizure detection, we also evaluated U-NeXt+, trained on an expanded dataset. Both U-NeXt variants and the ConvNeXt baseline were evaluated on two independent held-out seizure datasets, Helsinki and Cork, comprising 130 neonates, with recordings annotated by three experts. U-NeXt improved detection across all three event types, with particular gains in sensitivity for short events. U-NeXt+ achieved Matthews correlation coefficients of 0.862 and 0.891 on Helsinki and Cork, respectively, and improved hourly seizure burden correlations by 30-40% relative to ConvNeXt. U-NeXt+ met the criterion for expert-level performance on both…
PDF arXiv
arXiv cs.LGOct 23
Deep Learning-Based Prediction of HER2 Status from Whole-Brain MRI in Breast Cancer Brain Metastases
Sara Early, Chris Kang, Vibujithan Vigneshwaran, Kimberly Amador, Roger Y. Tsang +1
Purpose: Human epidermal growth factor receptor 2 (HER2) status is a key biomarker relevant for prognostication and treatment decision making in metastatic breast cancer. Thus, the aim of this work was to explore whether HER2 status can be predicted directly from whole-brain post-contrast T1-weighted (T1w) MRI scans in breast cancer brain metastases (BCBM) using deep learning, and whether providing explicit tumour segmentation information improves prediction. Methods: Using the publicly available BCBM-Radiogenomics dataset, we analysed 135 patients, including 70 HER2+ and 65 HER2- patients. Three approaches were implemented and evaluated: a conventional radiomics model, a simple fully convolutional network (SFCN) using T1w MRI scans alone, and an SFCN using T1w MRI scans with an expert-derived tumour mask as an additional input channel. Results: The radiomics model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.602 (95% confidence interval [CI]: 0.528--0.677) while the SFCN achieved a ROC-AUC of 0.667 (95% CI, 0.63--0.71) using T1w MRI scans alone, which increased to 0.692 (95% CI, 0.58--0.81) when an explicit tumour mask was additionally…
PDF arXiv
arXiv cs.CVOct 23
Cross-Architectural Feature Fusion for Coronary Artery Stenosis Detection in X-ray Angiography
Haobo Zhu, Mojtaba Lashgari, Jason Chai, Giovanni Luigi De Maria, Vicente Grau +1
Coronary artery disease remains a leading cause of mortality, with stenosis detection from invasive X-ray Coronary Angiography (XCA) critical for diagnosis. Manual interpretation is time-consuming, subjective, and variable, especially under low contrast and vessel overlap. Existing deep learning methods often rely solely on detection or segmentation, limiting performance. We propose a hybrid framework, Fusion-YOLOv11-L, combining a YOLOv11-L detector with a pretrained UNet++ via a channel attention gating module. This enables fusion of semantic and vessel-aware structural features, improving localisation of stenotic regions. The UNet++ is pretrained on ARCADE vessel segmentation dataset to provide anatomically informed representations. Experiments on the relabeled ARCADE (ARCADE*) dataset show improved performance over YOLOv11-L, achieving AP@50 of 0.585 and F1-score of 0.596. Cross-domain evaluation on a dataset from the Oxford Acute Myocardial Infarction (OxAMI) Study reveals performance degradation due to domain shift, mitigated using test-time adaptation and augmentation. Test-time augmentation improves AP@50 from 0.434 to 0.480, indicating better generalisability. These…
PDF arXiv
arXiv eess.SPOct 23
EMG Tokenization Preserves Movement Information at a Fraction of the Bitrate
Raul C. Sîmpetru, Amin Olamazadeh, Devon Rohlf, Vlad Cnejevici, Alessandro Del Vecchio
Wireless electromyography (EMG) interfaces must balance electrode coverage against transmission bandwidth. Here, we show that compact EMG tokens substantially reduce payload while retaining information for three-dimensional hand-pose decoding. We evaluate two causal tokenizers fitted without pose labels: an encoder pretrained to reconstruct EMG spectra and one based on SPAR, an engineered spectral-spatial representation of neighbouring electrodes. In personalized, within-session experiments with 320-channel EMG from nine participants, we compare tokenized and continuous features using matched decoder architectures and training procedures. Averaged across decoders, tokenization changed mean error by +0.01 mm for SPAR (95% confidence interval: -0.12 to 0.14) and +0.45 mm for the learned representation (0.10 to 0.82). Both increases met a 1 mm non-inferiority criterion. Stored token arrays reduced payloads 41-fold relative to raw EMG and 36-fold relative to continuous SPAR features. On sampled windows of the public emg2pose benchmark's test partition, absolute differences in mean error between learned tokens and continuous features were below 0.05 mm across 176 participants and in…
PDF arXiv
arXiv cs.CVOct 23
Hair Segmentation and Skin Characterization on Macroscopic Dermatological Images for Laser Treatment Parameterization
Alex Navarro Navarro, Marc Arriaga Hidalgo, Carlos Moreno Rubio, Maxime Talarmain, Noelia Vaquero Gallardo
Current light-based devices, such as laser hair removal, intense pulsed light (IPL), vascular laser therapy, and dermatologic phototherapy, are widely used in clinical dermatology but rely heavily on subjective manual parameterization or uncalibrated point-based colorimeters. To overcome these limitations, this work presents a unified system for hair segmentation and skin characterization in macroscopic dermatological images. Unlike most existing literature that relies on dermoscopic datasets captured under highly controlled conditions, our approach is specifically designed for macroscopic images that replicate real clinical laser treatments. The proposed pipeline integrates a custom-trained variant of the YOLOv9 deep learning architecture for instance segmentation and classical image processing within a single workflow to extract objective skin and hair parameters such as phototype, hair density, color, and thickness. A color calibration stage using spectrophotometric measurements ensures accurate feature estimation regardless of illumination conditions. Experimental results show competitive segmentation metrics (mAP@50 $\approx$ 0.80) on a custom dataset featuring short, shaved…
PDF arXiv
arXiv cs.CVOct 23
Ampe-DB: A Dataset for Human Activity and Interaction Analysis Using the Ghanaian Game Ampe
Ruth Senior Biney, Isaac Kojo Yedonu Aboah
Human activity recognition benchmarks underrepresent culturally situated forms of movement and interaction. We introduce Ampe-DB, a multimodal dataset based on Ampe, a traditional Ghanaian game involving coordinated jumping, leg movements, and two-person interaction. It contains 637 canonical clips from 15 participants, with RGB video, two-person pose representations, participant and pair metadata, four-class movement labels, and derived binary movement-relation labels. We evaluate four-class movement recognition and binary movement-relation classification using three protocols: a duplicate-safe fixed benchmark, four-fold pair-held-out evaluation, and participant-held-out diagnostics for participants with complete class coverage. Baselines comprise a frozen Kinetics-400-pretrained R(2+1)D-18 RGB encoder, a two-layer bidirectional GRU for pose sequences, and validation-selected RGB-pose late fusion. For four-class recognition, RGB macro-F1 falls from 0.955 on the fixed benchmark to 0.418 under pair-held-out evaluation and 0.384 under participant-held-out evaluation. Pose is more robust, achieving 0.957, 0.811, and 0.839 across the same protocols. Fusion reaches 0.976 on the fixed…
PDF arXiv