DermAgent orchestrates seven vision-language tools in a Plan-Execute-Reflect loop with dual-modality retrieval from 413k cases and a critic module to outperform GPT-4o by 17.6% in zero-shot dermatological diagnosis accuracy.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,
9 Pith papers cite this work, alongside 3,113 external citations. Polarity classification is still indexing.
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MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.
Rough-set analysis finds 16.4% of 305 concept profiles in Derm7pt inconsistent (306 images), capping hard CBM accuracy at 92.1%; symmetric filtering produces a 705-image consistent benchmark where EfficientNet-B5 reaches 0.90 label accuracy.
A CNN-based discrete diffusion method refines sparse contours from segmentation masks using simplified denoising steps and minimal post-processing, outperforming baselines on small medical and environmental datasets while running 3.5 times faster.
The authors introduce predicted-weighted balanced accuracy (pBA), a utility-weighted evaluation metric that uses predicted subconcept posteriors to reduce bias from within-class heterogeneity in imbalanced data.
FSS-TIs models cross-domain few-shot segmentation as an ODE process with Fourier-based spectral perturbations to create domain-agnostic features and enable effective fine-tuning on limited support samples.
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.
Fine-tuned MedGemma outperforms untuned GPT-4 in zero-shot medical image disease classification, achieving 80.37% versus 69.58% mean test accuracy with higher sensitivity for cancer and pneumonia.
citing papers explorer
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DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making
DermAgent orchestrates seven vision-language tools in a Plan-Execute-Reflect loop with dual-modality retrieval from 413k cases and a critic module to outperform GPT-4o by 17.6% in zero-shot dermatological diagnosis accuracy.
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MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.
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Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset
Rough-set analysis finds 16.4% of 305 concept profiles in Derm7pt inconsistent (306 images), capping hard CBM accuracy at 92.1%; symmetric filtering produces a 705-image consistent benchmark where EfficientNet-B5 reaches 0.90 label accuracy.
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Contour Refinement using Discrete Diffusion in Low Data Regime
A CNN-based discrete diffusion method refines sparse contours from segmentation masks using simplified denoising steps and minimal post-processing, outperforming baselines on small medical and environmental datasets while running 3.5 times faster.
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Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
The authors introduce predicted-weighted balanced accuracy (pBA), a utility-weighted evaluation metric that uses predicted subconcept posteriors to reduce bias from within-class heterogeneity in imbalanced data.
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Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals
FSS-TIs models cross-domain few-shot segmentation as an ODE process with Fourier-based spectral perturbations to create domain-agnostic features and enable effective fine-tuning on limited support samples.
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Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
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MedGemma 1.5 Technical Report
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.
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MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images
Fine-tuned MedGemma outperforms untuned GPT-4 in zero-shot medical image disease classification, achieving 80.37% versus 69.58% mean test accuracy with higher sensitivity for cancer and pneumonia.