Conference Paper2026
Efficient Multi-Class Thoracic Disease Detection on VinDr-CXR Using YOLOv12 with Class-Balanced Training and Anatomical TTA
Afzal Hossain Alif, Mojtoba Zaman Mantaka, Aiman Saad Hamid, Saadia Binte Alam
2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)
In: 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)
IEEE, pp. 1-6
Abstract
Chest X-ray (CXR) imaging remains the primary screening modality for thoracic diseases, yet automated systems frequently struggle with concurrent multi-pathology detection and precise localization of subtle or diminutive lesions. Contemporary methods such as YOLOv11-MFF have sought to address these limitations through elaborate architectural enhancements, including frequency-adaptive gates and multi-scale fusion modules. In this work, we contest the premise that such architectural complexity is a prerequisite for leading performance. We show that a standard state-of-the-art detection backbone, coupled with principled data-centric strategies, renders bespoke domainspecific modules unnecessary. Our framework integrates Inverse-Frequency Weighted Upsampling to counteract long-tail class distributions and Anatomically-Constrained Test-Time Augmentation (CXR-TTA) to exploit the bilateral symmetry inherent to the thoracic cavity. On the VinDr-CXR benchmark, our approach attains a mean Average Precision (mAP@0.5) of 0.442, substantially exceeding the prevailing state-of-the-art without any custom neural network components. These results affirm that disciplined data-centric training strategies can outperform intricate architectural engineering in medical object detection.
Keywords
Training (meteorology)Disease
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