medical imaging

topic20 events

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

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

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

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 The work appears under the cs.AI and cs.LG categories and has not yet been peer reviewed.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

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

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

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

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

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

Echo-CoPilot: Agentic Framework for Multi-View Echocardiography Interpretation

Researchers present Echo-CoPilot, a multi-perspective agentic framework designed to interpret echocardiography by combining temporal evidence from multiple views with quantitative measurements and guideline-based reasoning. The work targets a gap in existing foundation-model pipelines, which the authors say handle isolated subtasks and break down when tool outputs are incomplete or inconsistent. The paper is listed as an updated submission on arXiv (2512.09944v4).

agents/medical-ai/multimodal/research

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

papersTODAY 04:00 UTC

arXiv Paper Revisits Correctness Measures for Uncertainty Estimation in Clinical VLMs

A new preprint examines how correctness is defined when vision-language models are used to make clinical predictions from medical images and electronic health records. The authors argue that current uncertainty estimation methods may be evaluated in ways that do not reflect whether a prediction is actually reliable. The work targets safer deployment by improving how unreliable outputs are detected.

uncertainty-estimation/medical-ai/vision-language/research

papersTODAY 04:00 UTC

CyFM: Cylindrical Optimal Transport Method for Few-Step Complex-Valued Flow Matching

A new arXiv paper proposes CyFM, a technique that applies optimal transport on a cylindrical geometry to generate complex-valued signals in few sampling steps. Rather than treating data such as MRI scans and audio spectrograms as flat two-channel Euclidean inputs, the method models amplitude and phase separately. The authors argue this representation better matches the structure of complex signals for generative modelling.

flow-matching/optimal-transport/mri/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

papersSEP 11 04:00 UTC

arXiv Paper Proposes Classifier Reconstruction to Predict Synthetic Data Utility

A new arXiv preprint examines how well synthetic images help binary classification tasks where positive examples are scarce, as in medical imaging and industrial inspection. The authors propose measuring a "discriminative span" and reconstructing a classifier to predict how useful generated samples will be. The work aims to guide synthetic data selection in severely imbalanced settings.

synthetic-data/computer-vision/research/class-imbalance

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 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

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 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

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