Robust Multi-Source Covid-19 Detection in CT Images

Abstract

Deep learning models for COVID-19 detection from chest CT scans generally perform well when the training and test data originate from the same institution, but they often struggle when scans are drawn from multiple centres with differing scanners, imaging protocols, and patient populations. One key reason is that existing methods treat COVID-19 classification as the sole training objective, without accounting for the data source of each scan. As a result, the learned representations tend to be biased toward centres that contribute more training data. To address this, we propose a multi-task learning approach in which the model is trained to predict both the COVID-19 diagnosis and the originating data centre. The two tasks share an EfficientNet-B7 backbone, which encourages the feature extractor to learn representations that hold across all four participating centres. Since the training data is not evenly distributed across sources, we apply a logit-adjusted cross-entropy loss [1] to the source classification head to prevent underrepresented centres from being overlooked. Our pre-processing follows the SSFL framework with KDS [2], selecting eight representative slices per scan. Our method achieves an F1 score of 0.9098 and an AUC-ROC of 0.9647 on a validation set of 308 scans. The code is publicly available at https://github.com/Purdue-M2/-multisource-covid-ct.

Figures (5)

• Figure 1: Representative chest CT slices from two different scans across the four contributing medical centres, illustrating differences in field of view, contrast, tissue brightness, and windowing range that constitute the domain shift a multi-source model must overcome.
• Figure 2: Overview of the proposed pipeline. Raw CT scans from COVID and non-COVID cases undergo lung extraction and KDS sampling to yield eight slices per scan. These pass through a shared EfficientNet-B7 backbone and are aggregated via element-wise mean pooling into a single 2560-dimensional scan-level representation. Two task-specific heads operate on this vector. The COVID-19 detection head is trained with binary cross-entropy ($\ell_{ce}$) and the auxiliary source identification head with logit-adjusted cross-entropy ($\ell_{LA}$). The combined objective ${\ell} = \ell_{ce} + \gamma \cdot \ell_{LA}$ governs training, while only the detection head is used at inference.
• Figure 3: Data preprocessing pipeline. Raw CT scans for both COVID-19 and Non-COVID cases undergo lung extraction and KDS sampling to select 8 highly representative slices per scan.
• Figure 4: Per-source average F1 at each method's best $\gamma$. The LA variant yields the largest gains on Source 0 (+3.3 percentage point) and Source 2 (+4.9 percentage point). Source 3 scores remain low across all methods due to the absence of COVID samples in the validation split.
• Figure 5: Sensitivity to the source-loss weight $\gamma$. Final score of Multi-task + LA as $\gamma$ varies in $\{0.1,\,0.2,\,0.5,\,1.0\}$. The dashed line marks the BCE-only baseline ($\gamma\!=\!0$). Performance peaks at $\gamma\!=\!0.5$ (score $=0.8194$), where the source head and logit adjustment reinforce each other; both lower and higher weights degrade the score.