# MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction

arXiv: https://arxiv.org/abs/2502.00631 · HTML: https://mlsanity.com/paper/2502.00631/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [The Road Less Scheduled](https://mlsanity.com/paper/2405.15682/) (2024)
- [CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography](https://mlsanity.com/paper/2105.14711/) (2021)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [SegReg: Segmenting OARs by Registering MR Images and CT Annotations](https://mlsanity.com/paper/2311.06956/) (2023)
- [ESA: Annotation-Efficient Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2408.13491/) (2024)
- [MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule](https://mlsanity.com/paper/2409.14028/) (2024)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA](https://mlsanity.com/paper/2404.11525/) (2024)
- [MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection](https://mlsanity.com/paper/2409.00204/) (2024)
- [ReMix: Calibrated Resampling for Class Imbalance in Deep learning](https://mlsanity.com/paper/2012.02312/) (2020)
- [SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies](https://mlsanity.com/paper/2412.19990/) (2024)
- [GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation](https://mlsanity.com/paper/2501.12844/) (2025)
- [ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer](https://mlsanity.com/paper/2501.01392/) (2025)
- [Long-tailed Recognition by Learning from Latent Categories](https://mlsanity.com/paper/2206.01010/) (2022)

## Built on by (most cited first, 16 of 16)

- [Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2509.09119/) (2025)
- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [ShieldedCode: Learning Robust Representations for Virtual Machine Protected Code](https://mlsanity.com/paper/2601.20679/) (2026)
- [DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps](https://mlsanity.com/paper/2502.15885/) (2025)
- [SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation](https://mlsanity.com/paper/2506.08949/) (2025)
- [Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment](https://mlsanity.com/paper/2504.11515/) (2025)
- [EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer](https://mlsanity.com/paper/2605.01732/) (2026)
- [A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation](https://mlsanity.com/paper/2502.05396/) (2025)
- [GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation](https://mlsanity.com/paper/2505.03846/) (2025)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)
- [GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation](https://mlsanity.com/paper/2505.05520/) (2025)
- [SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation](https://mlsanity.com/paper/2604.17451/) (2026)
- [Dependency-Guided Code Generation: Structured Matrix Decomposition and Consistency-Guided Refinement](https://mlsanity.com/paper/2607.16692/) (2026)
- [Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation](https://mlsanity.com/paper/2607.16704/) (2026)
- [Bridging the Modality Gap in Long-Form Clinical Audio: A Comparative Study of Lightweight and Heavyweight End-to-End SOAP Generation](https://mlsanity.com/paper/2609.14467/) (2026)
- [Code Consistency Preference Optimization Verification for Language Model Alignment](https://mlsanity.com/paper/2609.19002/) (2026)

## Abstract (arXiv)

Bone density prediction via CT scans to estimate T-scores is crucial, providing a more precise assessment of bone health compared to traditional methods like X-ray bone density tests, which lack spatial resolution and the ability to detect localized changes. However, CT-based prediction faces two major challenges: the high computational complexity of transformer-based architectures, which limits their deployment in portable and clinical settings, and the imbalanced, long-tailed distribution of real-world hospital data that skews predictions. To address these issues, we introduce MedConv, a convolutional model for bone density prediction that outperforms transformer models with lower computational demands. We also adapt Bal-CE loss and post-hoc logit adjustment to improve class balance. Extensive experiments on our AustinSpine dataset shows that our approach achieves up to 21% improvement in accuracy and 20% in ROC AUC over previous state-of-the-art methods.