Divide Then Diagnose
PulseAugur coverage of Divide Then Diagnose — every cluster mentioning Divide Then Diagnose across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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New FCA-Guided framework offers perfect validity for AI breast cancer diagnosis explanations
Researchers have developed a novel framework called FCA-Guided Counterfactual (FCA-CF) to generate actionable explanations for multi-modal breast cancer diagnosis models. This framework uses Formal Concept Analysis to c…
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New BSC-Net improves coronary vessel segmentation for disease analysis
Researchers have developed BSC-Net, a novel framework based on ResNet-U-Net designed to improve the segmentation of small coronary vessels in X-ray coronary angiography. The network addresses challenges like imaging noi…
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New XAI method generates perceptible counterfactual examples using expert knowledge
A new research paper introduces DiCEf, an extension of the DiCE method for generating counterfactual examples (CFEs) in explainable AI (XAI). This enhanced method integrates expert knowledge through a fuzzy linguistic v…
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DICE researcher presents ASTRA paper on knowledge graph embeddings at ECML PKDD
Duygu Ekinci Birol, a researcher at DICE, presented the paper "ASTRA: Adaptive Structure-Aware Post-Hoc Alignment of Knowledge Graph Embeddings" at ECML PKDD 2026 in Naples. The research, co-authored by Mohamed Sherif, …
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New SpFiLM technique enhances brain MRI parcellation accuracy
Researchers have developed a new technique called Spatial Feature-wise Linear Modulation (SpFiLM) to improve the accuracy of automated brain parcellation, particularly for contrast-enhanced T1ce MRI scans. Traditional m…
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New methods improve fiber bundle segmentation in brain histology
Researchers have developed new methods for segmenting fiber bundles in tracer histology data, a crucial step for understanding brain connectivity. The study compares traditional pixel-overlap losses like BCE and Dice wi…
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DICE technique enhances text-to-image generation by refining embeddings
Researchers have developed a new technique called DICE (Distilling Classifier-Free Guidance into Text Embeddings) to improve text-to-image generation. DICE refines text embeddings to mimic the effects of classifier-free…
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AI model performance and human variability in cancer index assessment analyzed
Researchers have evaluated how variations in human interpretation and AI model performance affect the assessment of radiological Peritoneal Cancer Index (rPCI) scores using contrast-enhanced CT scans. The study found th…
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New framework automates medical imaging code generation
Researchers have developed AutoMedImg, a novel multi-agent framework designed to fully automate the generation of medical imaging processing code. This system operates in two phases: planning, which includes dataset ana…
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New AI method improves MRI segmentation reliability across domains
Researchers have developed a new method called CARD (Calibration via Agreement in Reverse Diffusion) to improve the reliability of AI segmentation models in medical imaging, particularly when dealing with out-of-domain …
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New FCom-DICE method conceals communities from graph neural networks
Researchers have developed a new method called FCom-DICE to protect sensitive communities within networks from being identified by graph neural networks (GNNs). This technique involves making small, utility-preserving m…
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New FS-JEPA method boosts KANs for medical image segmentation · 2 sources tracked
Researchers have developed a new method called Function-Space Joint-Embedding Predictive Learning (FS-JEPA) to improve the performance of Kolmogorov-Arnold Networks (KANs) in medical image segmentation. This approach tr…
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New adaptive prompting framework improves multi-organ ultrasound segmentation
Researchers have developed BAP-MOS, a novel framework for multi-organ ultrasound segmentation that addresses challenges with adjacent structures and localized boundary errors. The system employs an adaptive prompting st…
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New research highlights bias in echocardiographic AI models
Researchers have identified a "Conditioning-Availability Bias" in echocardiographic segmentation models, where auxiliary signals used during training are cleaner than those available at deployment. This shortcut learnin…
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New neural operator accelerates tFUS digital twin simulations
Researchers have developed tFUSOperator, a novel neural operator designed to accurately predict the intracranial acoustic field for transcranial focused ultrasound (tFUS) treatments. This approach addresses the computat…
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DARPA launches DICE project for 100,000 autonomous AI agents
DARPA is initiating a project called Decentralized Artificial Intelligence through Controlled Emergence (DICE) to develop a large-scale network of AI agents capable of independent thought and action. The project aims to…
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New methods quantify domain shift in echocardiography segmentation
Researchers have developed methods to quantify and predict domain shift in echocardiographic left ventricular segmentation, a key challenge for clinical deployment. Their study found that geometric inconsistencies, rath…
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Capgemini Leader Proposes DICE Framework for Integrated Customer Intelligence
Hemant Soni, a Digital Transformation Leader at Capgemini, proposes the Data-Integrated Campaign Engagement (DICE) framework to address the paradox of enterprises having more customer data but less AI-driven intelligenc…
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New recursive controller enhances lightweight polyp segmentation
Researchers have developed a novel recursive controller for lightweight polyp segmentation, operating directly on backbone logits to refine predictions. This controller aggregates discrepancy and uncertainty evidence to…
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New framework DICE enhances AI pathology model reliability with uncertainty estimation
Researchers have developed a new framework called DICE to improve the reliability of pathology foundation models (PFMs) for whole-slide image analysis. This framework ensembles multiple frozen PFMs and uses their disagr…