# DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening

arXiv: https://arxiv.org/abs/2508.02741 · HTML: https://mlsanity.com/paper/2508.02741/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Clustering with t-SNE, provably](https://mlsanity.com/paper/1706.02582/) (2017)
- [Towards Automated Tuberculosis detection using Deep Learning](https://mlsanity.com/paper/1801.07080/) (2018)
- [Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations](https://mlsanity.com/paper/2501.00778/) (2025)

## Built on by (most cited first, 6 of 6)

- [SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis](https://mlsanity.com/paper/2603.21010/) (2026)
- [Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate](https://mlsanity.com/paper/2604.11258/) (2026)
- [Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening](https://mlsanity.com/paper/2604.05620/) (2026)
- [Attention-Guided Flow-Matching for Sparse 3D Geological Generation](https://mlsanity.com/paper/2604.09700/) (2026)
- [TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation](https://mlsanity.com/paper/2606.26874/) (2026)
- [RoleMix: Unifying Sequential and Non-Sequential Features via Semantic Tokenization for Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2607.22700/) (2026)

## Abstract (arXiv)

Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.