Due to the complex biological and physical mechanisms, the correlations between the classification objects of clinical tasks and the medical imaging phenotype are always ambiguous and implied, which makes it difficult to train a powerful diagnostic convolutional neural network (CNN) model efficiently. In this study, we propose a generic multi-task learning (MTL) CNN framework to achieve higher classification accuracy and better generalization. The proposed framework is designed to carry out the major diagnostic task and several auxiliary tasks simultaneously. It encourages the models to learn more beneficial representation following the underlying relation among patients’ clinical characteristics, obvious imaging findings and quantitative imaging phenotype. We evaluate our approach on two clinical applications, namely advanced gastric cancer (AGC) serosa invasion diagnosis and discrimination of lung invasive adenocarcinoma manifesting as ground-glass nodule (GGN). Two datasets are utilized, which contain 357 AGC patients’ venous phase contrast-enhanced CT volumes and 236 GGN patients’ non-contrast CT volumes respectively. Several subjective CT morphology characteristics and common clinical characteristics are collected and used as the auxiliary tasks. To evaluate the generality of our strategy, CNNs with and without natural image-based pre-training are successively incorporated into the framework. The experimental results demonstrate that the proposed MTL CNN framework is able to improve the diagnostic performance significantly (7.4%-12.8% AUC increase and 3.5%-7.9% accuracy increase).