Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis

• URL: http://arxiv.org/abs/2507.19230v1
• Date: Fri, 25 Jul 2025 12:55:48 GMT
• Title: Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis
• Authors: Niels Rocholl, Ewoud Smit, Mathias Prokop, Alessa Hering,
• Abstract summary: This paper investigates the performance of the ULS23 segmentation model in a longitudinal context.<n>We identify two critical, interconnected failure modes: a sharp degradation in segmentation quality in follow-up cases due to inter-scan registration errors, and a subsequent breakdown of the lesion correspondence process.
• Score: 0.5537760992845262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Longitudinal lesion analysis is crucial for oncological care, yet automated tools often struggle with temporal consistency. While universal lesion segmentation models have advanced, they are typically designed for single time points. This paper investigates the performance of the ULS23 segmentation model in a longitudinal context. Using a public clinical dataset of baseline and follow-up CT scans, we evaluated the model's ability to segment and track lesions over time. We identified two critical, interconnected failure modes: a sharp degradation in segmentation quality in follow-up cases due to inter-scan registration errors, and a subsequent breakdown of the lesion correspondence process. To systematically probe this vulnerability, we conducted a controlled experiment where we artificially displaced the input volume relative to the true lesion center. Our results demonstrate that the model's performance is highly dependent on its assumption of a centered lesion; segmentation accuracy collapses when the lesion is sufficiently displaced. These findings reveal a fundamental limitation of applying single-timepoint models to longitudinal data. We conclude that robust oncological tracking requires a paradigm shift away from cascading single-purpose tools towards integrated, end-to-end models inherently designed for temporal analysis.

Related papers

• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics [17.682803546212824]
  We propose a Longitudinal Intra-Patient Tracking (LIPT) scheme to capture the trajectory of relative symptomatic changes within individuals.<n>Central to this framework is a Sequential Personalised (PSE) which transforms longitudinal speech recordings into context-aware latent representations.<n> Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches.
  arXiv  Detail & Related papers  (2026-02-23T10:19:17Z)
• Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation Across Acute and Chronic Lesions [0.0]
  We evaluate stroke lesion segmentation using the nnU-Net framework across multiple publicly available MRI datasets.<n>Across stroke stages, models showed robust generalisation, with segmentation accuracy approaching reported inter-rater reliability.<n>In acute stroke, DWI-trained models consistently outperformed FLAIR-based models, with only modest gains from multimodal combinations.<n>In chronic stroke, increasing training set size improved performance, with diminishing returns beyond several hundred cases.
  arXiv  Detail & Related papers  (2026-01-13T16:29:20Z)
• XAI-Driven Diagnosis of Generalization Failure in State-Space Cerebrovascular Segmentation Models: A Case Study on Domain Shift Between RSNA and TopCoW Datasets [0.5735035463793009]
  We present a rigorous, two-phase approach to diagnose the generalization failure of state-of-the-art State-Space Models (SSMs)<n>We quantified the model's focus by measuring the overlap between its attention maps and the Ground Truth segmentations.<n>Our analysis proves the model failed to generalize because its attention mechanism abandoned true anatomical features in the Target domain.
  arXiv  Detail & Related papers  (2025-12-16T00:34:32Z)
• TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification [10.329406702659123]
  Tumor segmentation and diagnosis in contrast-enhanced Computed Tomography (CT) rely heavily on the physiological dynamics of contrast agents.<n>Existing deep learning approaches typically treat missing phases as absent independent channels, ignoring the inherent temporal continuity of hemodynamics.<n>We propose Time Attenuated Representation Disentanglement (TARDis), a novel physics-aware framework that redefines missing modalities as missing sample points on a continuous Time-Attenuation Curve.
  arXiv  Detail & Related papers  (2025-12-04T08:44:50Z)
• SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery [44.119171920037196]
  We develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery.<n>We compare traditional ML models with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention.<n>Performance was evaluated using the coefficient of determination (R2) and key predictors were identified using explainable AI.
  arXiv  Detail & Related papers  (2025-07-15T01:18:28Z)
• Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach [0.0]
  We propose a novel imputation framework using Self-Attention-based Imputation networks for time series.<n>We refine the imputed data using a custom made autoencoder, tailored to represent smooth pursuit eye movement sequences.<n>Results show a significant improvement in the accuracy of reconstructed eye movement sequences.
  arXiv  Detail & Related papers  (2025-05-31T13:10:30Z)
• Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia [44.39545678576284]
  This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach.<n>The first stage converts time-series activities into text sequences encoded by a pre-trained language model.<n>This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form.
  arXiv  Detail & Related papers  (2025-02-13T10:57:25Z)
• Generalizable automated ischaemic stroke lesion segmentation with vision transformers [0.7400397057238803]
  Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke.<n>Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics.<n>Here, we present a high-performance DWI lesion segmentation tool addressing these challenges.
  arXiv  Detail & Related papers  (2025-02-10T19:00:00Z)
• Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains [0.90668179713299]
  We show that the model achieves on-par performance with strong fully supervised baseline models. We also observe a performance decrease for both fully supervised and weakly supervised models when tested on unseen data domains.
  arXiv  Detail & Related papers  (2024-11-04T12:24:33Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting [2.0168790328644697]
  We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. We achieve superior scores in lesion segmentation as well as lesion detection as compared to state-of-the-art longitudinal and single timepoint models across two datasets.
  arXiv  Detail & Related papers  (2024-09-20T11:30:54Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading [72.45699658852304]
  This paper proposes a novel approach to train a generative Diffusion Autoencoder model as an unsupervised feature extractor. We model fracture grading as a continuous regression, which is more reflective of the smooth progression of fractures. Importantly, the generative nature of our method allows us to visualize different grades of a given vertebra, providing interpretability and insight into the features that contribute to automated grading.
  arXiv  Detail & Related papers  (2023-03-21T17:16:01Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data [72.99534552950138]
  The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. accurate segmentation of splenic vascular injury is challenging for the following reasons.
  arXiv  Detail & Related papers  (2022-01-04T02:35:56Z)
• Clustering Left-Censored Multivariate Time-Series [2.4493299476776778]
  We focus on mitigating the interference of left-censorship in the task of clustering. We develop a deep generative, continuous-time model of time-series data that clusters while correcting for censorship time. We demonstrate accurate, stable, and interpretable results on synthetic data.
  arXiv  Detail & Related papers  (2021-02-13T21:22:40Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.