#medical-imaging

40 curated events

papersTODAY 04:00 UTC

ProtoCAM: Few-Shot Prototypical Learning for Breast Lesion Classification in Ultrasound

Researchers propose ProtoCAM, a method that combines prototypical networks with mask guidance to classify breast lesions in ultrasound images using only a small number of labeled examples. The approach is designed to be interpretable, addressing the difficulty of building reliable deep learning models for ultrasound analysis, especially in patients with dense breast tissue. The work is published as an arXiv preprint.

medical-imaging/few-shot-learning/interpretability/arxiv

papersTODAY 04:00 UTC

Radiomics Method Separates Voxel Geometry From Interpolation Effects in CT/MRI

A new arXiv preprint addresses a known pitfall in radiomic texture analysis: features are typically calculated over voxel-index neighborhoods, which assumes uniform spatial spacing. In anisotropic scans, this assumption mixes true voxel geometry with signal changes introduced by interpolation. The authors propose a physically aware approach that avoids interpolation so the two effects can be disentangled.

medical-imaging/radiomics/research/arxiv

papersTODAY 04:00 UTC

SpermYOLO: YOLO-Based Detector for Sperm and Impurity Detection in Microscopy

Researchers present SpermYOLO, a coordinated YOLO-based detection model aimed at locating sperm cells in microscopic images for computer-assisted semen analysis. The work targets difficulties such as densely packed cells, visually similar artifacts, and sperm-like impurities that complicate automated detection. The paper is an arXiv preprint and reports on detection accuracy and efficiency.

object-detection/medical-imaging/yolo/computer-vision

papersTODAY 04:00 UTC

S3-Tracker: Self-Supervised Tissue Tracking in Endoscopic Video

Researchers present S3-Tracker, a self-supervised method for tracking points in endoscopic surgical video, a task needed for aligning live footage with preoperative images during robot-assisted procedures. The approach uses contrastive random walks to learn tracking without manual annotations, aiming to stay reliable under soft-tissue deformation. The work targets computer-assisted intervention and autonomous robotic surgery.

self-supervised/medical-imaging/computer-vision/surgical-robotics

papersTODAY 04:00 UTC

Real-time foundation model for endoscopy supports task-specific fine-tuning

Researchers present woma, a foundation model trained without labels on roughly one million gastrointestinal endoscopy frames. Task-specific models are then fine-tuned from this base, and the authors describe a systematic design intended for production deployment, including requirements and performance targets. The work targets real-time use in clinical endoscopy workflows.

foundation-models/medical-imaging/fine-tuning/real-time

papersTODAY 04:00 UTC

arXiv Paper Proposes Counterfactual Medical Images for Dataset Augmentation

A new arXiv preprint examines using counterfactual image generation to augment training data for medical image analysis. The authors argue that biased datasets produce biased models with limited clinical usefulness, and that synthetic counterfactual images can help offset those biases. The work is announced as a new submission in the cs.LG category.

medical-imaging/data-augmentation/counterfactuals/bias

papersTODAY 04:00 UTC

Spectral and Activation Clustering Combined for Backdoor Detection in Medical Imaging Models

A new arXiv paper proposes combining spectral signatures of model weights with clustering of internal activations to detect backdoors planted in healthcare imaging models during training. The authors frame the work around sector-level guidance that names model poisoning and adversarial manipulation as concerns for clinical machine learning deployments. The paper covers the method, its implementation, and an evaluation of detection performance.

backdoor-detection/medical-imaging/security/arxiv

papersTODAY 04:00 UTC

CANAL adds channel-aware noise allocation for private medical image segmentation

A new arXiv paper proposes CANAL, a method that lets hospitals train segmentation models together without sharing patient scans. It uses knowledge distillation to transfer learned feature representations, injecting differential-privacy noise in a channel-aware way so that the privacy budget is spent where it matters most. The approach targets medical image segmentation, where complementary data sits in separate institutions that cannot legally exchange it.

differential-privacy/medical-imaging/knowledge-distillation/federated-learning

papersTODAY 04:00 UTC

Operator-Informed Gaussian Processes Model Complex Helmholtz Wavefields

Researchers present an approach that embeds knowledge of the Helmholtz operator into Gaussian process models so they can reconstruct complex-valued wavefields, including the dissipative case where the squared wavenumber becomes complex. The method is tested first on synthetic benchmarks and then applied to in vivo brain elastography, where it infers tissue properties from limited, noisy measurements. The work emphasizes uncertainty quantification alongside field estimation.

gaussian-processes/medical-imaging/elastography/research

papersTODAY 04:00 UTC

PyRadiomics extension adds voxel-spacing awareness for anisotropic texture analysis

Researchers implemented and validated a modification to the PyRadiomics library that accounts for voxel spacing when computing texture features from anisotropic CT and MRI scans. In such acquisitions, equal voxel offsets can correspond to different physical distances, which the extension aims to correct. The work targets more consistent radiomic feature extraction in clinical imaging pipelines.

medical-imaging/radiomics/open-source/software

papersTODAY 04:00 UTC

DICOM Standard Proposed for Sharing Image-Derived Data in Computational Pathology

A new arXiv paper examines how standardized image-derived data can be shared in computational pathology using DICOM. The authors note that computational pathology research depends on large, diverse datasets, and that past efforts have focused heavily on collecting and centralizing imaging data. The work argues for common standards to make such data easier to exchange and reuse across institutions.

computational-pathology/dicom/medical-imaging/data-sharing

papersTODAY 04:00 UTC

Paper measures and mitigates template collapse in 3D CT report generation

A new arXiv paper examines how 3D medical vision-language models can write fluent radiology-style reports while still missing critical findings and producing highly repetitive output. The authors characterize this behavior, which they call template collapse, noting that models default to generic phrasing that under-reports rare pathologies. They then propose ways to measure the problem and reduce it.

medical-imaging/vision-language-models/evaluation/safety

papersTODAY 04:00 UTC

Benchmark Compares General-Purpose Vision Models vs Specialized Medical Segmentation Models

A new arXiv preprint introduces GP-VM×SMA, a benchmarking study that evaluates general-purpose vision models alongside architectures designed specifically for 2D medical image segmentation. The work frames medical image segmentation as a core part of computer-assisted diagnosis and clinical decision support, where domain-specific designs have dominated for the past decade. The authors position the benchmark as a way to measure how well broadly trained vision models handle this specialized task relative to purpose-built alternatives.

benchmark/medical-imaging/vision-models/research

papersTODAY 04:00 UTC

EasyLens Boosts Subtle Lesion Detection in Medical Vision-Language Models

Researchers present EasyLens, a plug-and-play method that amplifies the representation of subtle lesions in medical vision-language models without requiring any additional training. The approach targets a known weakness of such models, whose clinical usefulness is limited by low sensitivity to faint or small abnormalities. According to the paper, the technique can be added to existing pipelines to improve lesion detection and related report generation tasks.

medical-imaging/vision-language/training-free/arxiv

papersTODAY 04:00 UTC

Reinforcement Learning Optimizes CT Protocols via Virtual Imaging Trials

Researchers apply reinforcement learning to CT protocol tuning, where acquisition and reconstruction settings interact in ways that make brute-force testing impractical. Their framework relies on virtual imaging trials to search the parameter space and balance diagnostic image quality against radiation exposure. The work appears as a cross-listed arXiv submission in machine learning.

reinforcement-learning/medical-imaging/arxiv/healthcare

papersTODAY 04:00 UTC

JSolver Jointly Estimates Spectrum and Materials from Single-Energy CT Projections

A new arXiv paper presents JSolver, a method that performs multi-material decomposition using only single-energy CT projection data rather than requiring spectral CT scanners. Conventional decomposition approaches depend on spectral hardware and pre-measured spectra, which limits their availability in routine clinical settings. The work aims to make quantitative tissue-composition reconstruction feasible on standard single-energy systems.

medical-imaging/ct/research/arxiv

papersTODAY 04:00 UTC

arXiv Paper Proposes Concept-Grounded Reasoning for Medical Imaging Reports

A new arXiv preprint introduces an approach that combines multimodal large language models with prompt-driven localization to produce interpretable structured reports from medical images such as ultrasound and X-ray. The method grounds reasoning in clinical concepts, aiming to align generated findings with standardized diagnostic criteria. The work appears under the cs.AI and cs.LG categories and has not yet been peer reviewed.

medical-imaging/multimodal/interpretability/arxiv

papersTODAY 04:00 UTC

Conformal Prediction Method Targets Ambiguous Class Boundaries in Medical Imaging

A new arXiv paper addresses a common problem in medical image classification: transitional categories whose features overlap with neighboring classes, creating fuzzy decision boundaries. The proposed approach, adaptive conformal redistribution, aims to sharpen uncertainty-aware prediction sets so they remain informative when a case sits between two diagnoses. Conformal prediction normally provides coverage guarantees for such sets, and the work adapts that framework to inter-class transitions.

conformal-prediction/medical-imaging/uncertainty/research

papersTODAY 04:00 UTC

Unified vision-language model targets PSMA PET/CT reporting, VQA, and lesion segmentation

Researchers present a single vision-language model designed to handle three prostate cancer imaging tasks at once: generating PET/CT reports, answering visual questions, and segmenting lesions. The work argues that prior PET/CT AI systems typically tackle these tasks in isolation, and that combining them may improve clinical usefulness.

medical-imaging/vision-language/multimodal/arxiv

papersTODAY 04:00 UTC

Paper grades cone beam CT report generation mostly by LLM entailment score

A new arXiv preprint describes a method for generating maxillofacial radiology reports from cone beam CT scans, evaluated with a composite metric that assigns 80% of the weight to a large language model's judgment of factual entailment and 20% to lexical overlap. The abstract notes that only the lexical portion of this objective is fully observable or directly optimizable. The work sits at the intersection of medical imaging report generation and LLM-based evaluation.

medical-imaging/evaluation/llm-judge/research

papersTODAY 04:00 UTC

arXiv Paper Adds Anatomical Grounding to Alzheimer's MRI Classification

A new arXiv preprint introduces a multimodal contrastive learning method for staging Alzheimer's disease from structural MRI scans. The approach aims to keep model attention on anatomically relevant brain regions and to reduce reliance on clinical table variables that may leak or not generalize. It combines anatomical grounding with leakage-aware training for the classification task.

medical-imaging/alzheimers/multimodal/contrastive-learning

papersTODAY 04:00 UTC

Calibrated Uncertainty Maps for Radiative Gaussian Splatting in Sparse-View CT

Researchers examine whether uncertainty maps reliably indicate where a sparse-view CT reconstruction is erroneous, and find a discrepancy between whole-volume scoring and localization of errors within the imaged object. They derive analytic, clamp-aware moments to produce calibrated uncertainty estimates for radiative Gaussian splatting reconstructions. The work suggests standard variance-based uncertainty is not equivalent to an error map in this setting.

medical-imaging/uncertainty/gaussian-splatting/sparse-view-ct

papersTODAY 04:00 UTC

OCT-FedSIR framework addresses annotation noise in federated eye imaging

A new arXiv paper introduces OCT-FedSIR, a federated learning approach for ophthalmic imaging that does not require patient data to be centralized. The work targets unreliable annotation labels across participating institutions, where differences in disease prevalence and class composition can degrade model trustworthiness. It is cross-listed under cs.AI and cs.LG.

federated-learning/medical-imaging/annotation-noise/privacy

papersTODAY 04:00 UTC

arXiv Paper Benchmarks Intra-Patient 3D Deformable Multimodal Image Registration

A new arXiv preprint introduces a benchmarking study for 3D deformable multimodal image registration focused on aligning scans from the same patient. The work addresses the difficulty that matching anatomical structures appear with very different intensities across imaging modalities, which complicates clinical registration workflows. It is announced as a cross-listed submission in the cs.AI category.

medical-imaging/registration/benchmark/arxiv

papersTODAY 04:00 UTC

MedSAM-3: Text-Promptable Model for Medical Image Segmentation

A new arXiv paper presents MedSAM-3, a medical image segmentation model that takes text prompts as input. The authors argue that existing segmentation methods generalize poorly and require extensive, time-consuming manual annotation whenever they are applied to a new clinical task. The work appears as a replacement cross-listing on arXiv (2511.19046v2).

medical-imaging/segmentation/text-prompt/research

papersSEP 11 04:00 UTC

Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation

A new arXiv paper examines where and how much neural networks forget when trained sequentially on gynecological medical imaging tasks. The authors analyze forgetting layer by layer to identify which parts of a segmentation model should be preserved and which can be adapted. The work targets scenarios where consecutive clinical tasks vary in imaging modality and anatomy.

medical-imaging/continual-learning/segmentation/research

papersSEP 11 04:00 UTC

Mammography Risk Model Relies on Privileged History Distillation

A new arXiv paper proposes a method for predicting future breast cancer risk from longitudinal mammography screening data. The approach, called privileged history distillation, addresses the drop in performance that current models show when a patient's prior examinations are not available. It appears to be a revised submission (v2) to arXiv's machine learning category.

medical-imaging/risk-prediction/arxiv/machine-learning

papersSEP 10 04:00 UTC

Deep Learning Method Corrects Temporal Drift in Ultrasound-Based Myocardial Strain Tracking

A new preprint introduces a deep learning approach for tracking heart muscle motion in ultrasound images that embeds physiological constraints into the model. The design is intended to reduce temporal drift across the cardiac cycle, improving the reliability of strain measurements used to assess cardiac function. The work appears on arXiv as paper 2609.09577.

medical-imaging/healthcare/deep-learning

papersSEP 10 04:00 UTC

Method reconstructs volumetric CT scans from single chest X-rays via multi-pass blended learning

Researchers propose a multi-pass, multi-view blended learning approach for generating full 3D chest CT volumes from a single 2D chest radiograph. The task is an ill-posed inverse problem, made harder by the limited availability of paired X-ray and CT training data. The paper builds on earlier methods that relied on digitally reconstructed radiographs to overcome data scarcity.

medical-imaging/ct-synthesis/research/deep-learning

papersSEP 10 04:00 UTC

PSCT-Net Reconstructs Pediatric Skull CT from Sparse X-rays to Reduce Radiation Risk

Researchers have introduced PSCT-Net, a neural method that builds 3D skull CT volumes from sparse bi-planar X-rays, offering a lower-dose option for diagnosing craniofacial conditions in children. The approach pairs a differentiable back-projection module with attention-guided refinement to make the highly ill-posed reconstruction more geometry-aware.

medical-imaging/3d-reconstruction/healthcare/deep-learning

papersSEP 10 04:00 UTC

PRIME-SVR: Physics-informed slice-to-volume reconstruction for fetal brain T2 mapping

Researchers have introduced PRIME-SVR, a physics-informed implicit reconstruction method that turns motion-corrupted 2D fetal MRI slices into high-resolution 3D brain volumes with quantitative T2 mapping. The approach builds on standard slice-to-volume reconstruction by adding multi-echo signal modeling, targeting improved quantitative imaging of the developing fetal brain.

medical-imaging/mri/fetal-health/deep-learning

papersSEP 10 04:00 UTC

Researchers Revisit Statistical Color Matching for Robust Medical Image Classification

A new arXiv paper proposes statistical color matching as a simple, low-risk technique for keeping medical image classifiers accurate when deployment conditions diverge from training, such as hardware differences, color variation, and changing patient demographics. The authors argue that common fixes like color jittering do not provide enough diversity, and reposition this overlooked method as a more sustainable path to domain generalization.

medical-imaging/domain-generalization/computer-vision/robustness

papersSEP 10 04:00 UTC

Reference-Free Agreement Method Flags Unreliable Polyp Segmentation Models

Researchers propose Referee-Based Quality Estimation, a framework that scores polyp segmentation output without ground-truth labels by measuring how much a primary model agrees with other models. The approach is intended as a deployment-time signal that catches silent failures during real-time colonoscopy, when annotations are not available at inference. The work is published as an arXiv preprint.

medical-imaging/segmentation/reliability/arxiv

papersSEP 10 04:00 UTC

SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI

Researchers present SA-Profile, an automated approach for measuring the femoral sulcus angle from super-resolution knee MRI scans. The sulcus angle helps characterize trochlear dysplasia, a femoral abnormality linked to anterior knee pain and patellar instability, but is traditionally assessed by hand on a single axial slice. Automating this profiling aims to make trochlear morphology assessment more consistent and reproducible.

medical-imaging/mri/orthopedics/research

papersSEP 10 04:00 UTC

LeCor: Meta-Learned Test-Time Training for Interactive 3D Lung Tumour Segmentation

A new arXiv paper introduces LeCor, a method that uses meta-learned test-time training for interactive 3D segmentation of lung tumours on CT scans. Since outlining lung tumours takes up a large share of radiotherapy planning time, the approach lets clinicians iteratively correct contours proposed by a model. The work builds on promptable foundation models for segmentation in the medical imaging context.

medical-imaging/segmentation/research/healthcare

papersSEP 10 04:00 UTC

LightMedSeg-ISLES: stroke lesion segmentation with 81x fewer parameters than nnU-Net

A new arXiv paper introduces LightMedSeg-ISLES, a 1.26-million-parameter pipeline for segmenting stroke lesions in T1-weighted MRI scans. The authors position it as a lighter alternative to large networks and ensembles like nnU-Net, citing an 81-fold reduction in parameters. The smaller footprint is intended to ease storage and inference demands for clinical deployment.

medical-imaging/segmentation/efficiency/research

papersSEP 10 04:00 UTC

RAU: Reference-based anatomical understanding for vision language models

Researchers introduce RAU, a reference-based approach that enables vision language models to identify, localize, and segment anatomical structures in medical images. By working from reference images instead of large volumes of expert annotations, the method addresses the shortage of labeled data that has slowed progress in medical image analysis.

medical-imaging/vision-language-models/segmentation/research

papersSEP 12 04:00 UTC

Instance segmentation models support automated multi-class wound assessment

A new arXiv paper presents an approach to automated wound care that combines dedicated instance segmentation models for detecting wound boundaries with multi-class classification. The authors argue that existing AI systems for wound analysis tend to be narrow in scope, and propose handling boundary detection and wound typing as separate, specialized tasks. The method targets clinical decision support in both chronic and acute wound management.

medical-imaging/segmentation/computer-vision/healthcare

papersSEP 12 04:00 UTC

GRIPNet uses Gaussian radial intensity prior for pulmonary nodule detection in CT

A new arXiv paper introduces GRIPNet, a detection architecture that incorporates a Gaussian radial intensity prior to help locate pulmonary nodules in CT scans. The authors target lesions smaller than six millimeters, which remain difficult for existing detectors despite being key to early lung cancer diagnosis through low-dose CT screening. The work is a research contribution rather than a released product.

medical-imaging/ct-scans/deep-learning/arxiv

papersSEP 12 04:00 UTC

Study Compares Pre-trained CNNs for Melanoma Detection

A new arXiv preprint benchmarks several pre-trained convolutional neural networks on the task of separating melanoma from other skin lesions. The authors frame the problem around the difficulty of visual similarity between lesion types and variability in imaging, which complicates early diagnosis. The comparison evaluates how well these existing image models transfer to this clinical classification setting.

medical-imaging/cnn/benchmarks/computer-vision