Frequency-Aware Multi-Head Learning for Long-Tailed Medical Concept Detection in ImageCLEFmed Caption 2026

UIT_Worker at ImageCLEFmedical 2026: DenseNet-121 Full-Fit Medical Concept Detection with Asymmetric Loss

Results indicate that a carefully optimized ImageNet-initialized DenseNet-121 with imbalance-aware learning provides an effective and reliable solution for extreme long-tail medical concept detection.

DS-MED Lab at ImageCLEFmedical Caption 2026: Clinical-Aware Routing for Hallucination Suppression and Pathology-Focused Medical Image Captioning

An imbalance-aware class weighted TernausResnet architecture for high-fidelity medical image classification

CWTRNet: A Class weighted TernausResnet framework with adaptive optimization is proposed in this work for high reliability medical image classification. Deep learning(DL) models have made significant advancements in the analysis of medical images, especially in diagnosis and classification. TernausNet, on the other h...

DeepLens at ImageCLEFmedical 2026: A Continuation of CNN-Based Medical Concept Detection

What Matters for Lightweight Multi-Label Chest X-Ray Classification? A Controlled Multi-Seed Study of Attention, Loss, Width and Resolution

Attention modules and imbalance-aware losses are often credited with gains in multi-label chest X-ray classification, but their effects are seldom separated from training length, model width, input resolution and seed-to-seed variation. We integrate three existing components, squeeze-and-excitation (SE) channel attenti...

Clinical-context guidance and hyperbolic prototype learning for long-tailed radiology report generation.

Automated radiology report generation (RRG) can reduce the reporting burden, but existing methods remain limited in their ability to integrate clinical context effectively at the feature level. They also struggle with long-tailed disease distributions, which can hinder recognition of infrequent findings. To address the...