#segmentation

10 curated events

papersTODAY 04:00 UTC

Deep learning segmentation framework detects fin whale calls in ocean-bottom seismic data

Researchers present a semantic segmentation deep learning framework for large-scale bioacoustic detection, applied to fin whale calls recorded by ocean-bottom seismometers. The approach repurposes seismic instruments, originally deployed for geophysics, as a broad-area passive acoustic monitoring network. This could expand monitoring of baleen whales over long time spans and wide ocean regions.

bioacoustics/deep-learning/marine-monitoring/segmentation

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 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

Vision-language reinforcement learning enables scalable segmentation for clinical tasks

A new arXiv paper describes a vision-language reinforcement learning framework for on-demand analysis of medical images across a range of clinical applications. It focuses on delineating tumors and nearby organs at risk, a step central to radiotherapy planning, surgery, and treatment response assessment that currently demands significant expert time. The authors position the method as addressing the limitations of existing AI systems in this domain.

medical-ai/healthcare/segmentation/vision-language

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

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

Study Quantifies Text Branch Sensitivity in Medical Vision-Language Segmentation

Researchers on arXiv examine whether clinical text inputs genuinely drive pixel-level predictions in pretrained vision-language models for medical image segmentation. They propose an evidence-decoupling approach to characterize the sensitivity of the text branch, aiming to clarify the real contribution of textual information to segmentation outputs.

medical-ai/vision-language/segmentation/interpretability

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