## 4 Conclusions and Future Work  
In this paper, we propose a semi-supervised learning model (C\(q\)SL) that features multiple novel loss functions, including mutual information minimization (MIM), which minimizes the mutual information between the domain-invariant as well as domain-specific features. Empirically, we show that disentanglement with mutual information can improve the performance of the segmentation accuracy, while combined with an adversarial and a reconstruction block. Our novel use of total loss function enforces the network to capture both the spatial and intensity information. Our weighted soft focal loss can minimize the class imbalance problem by applying varying weights over different classes along with a modulating term. We apply the proposed model to cardiac image segmentation tasks with varying proportion of labeled data.  
Our proposed C\(q\)SL model achieves 85% accuracy, significantly outperforming other baselines. We incrementally add each component, aiming to study their effectiveness on the final results: (model **I**: only a GAN architecture (Figure 3c); model **II**: GAN + reconstruction (Figure 3c,d); model **III**: GAN + reconstruction + disentangled block (Figure 3a-d).  
In light of consistency, all four implemented C\(q\)SL variants are evaluated and compared to the baselines, but as shown in Tables 1-3, the first variant (\({}^{1}\)C\(q\)S) performs best and hence it is deemed as the most suitable and recommended C\(q\)SL framework.  
The experimental results reported in this manuscript show that the proposed _CqSL_ framework outperforms semi-supervised learning with GANs [56] as well as fully supervised-type models when using as little as even 1% labeled data and display similar performance and comparable accuracy when employing more than 50% labeled data. Unlike these, we use adversarial-Geman-McClure (adv-GM) loss to force mask generation to be spatially aligned with the image. Furthermore, we discover that the semi-supervised segmentation approach of [PERSON] et al. [18] obtains results slightly inferior to ours. [PERSON] et al. reported that their adversarial model achieved a 80.63% accuracy when trained on 20% labeled data using the ACDC dataset, whereas our model achieved a 81.44% accuracy under similar training conditions.  
Figure 17: Empirical analysis showing the effect of different loss functions on the 2017 STACOM ACDC dataset. The significant reduction of total loss in C\(q\)SL (in red) suggests the best performing model with best learned features.  
Hence, the proposed method is the first to achieve significant performance for 4D cine cardiac MRI image segmentation with very minimal annotated data, specifically 1% of the training dataset. This is a key feature of the proposed work and hence a significant contribution to the medical (cardiac, in particular) image segmentation, as access to large amounts of expert-annotated ground truth imaging data is expensive in the medical field. Nevertheless, here we demonstrate that _CqSL_ can still yield segmentation accuracy superior to other semi-supervised methods while requiring minimal annotated data for training.  
Conceptualization, S.M.K.H. and C.A.L.; methodology, S.M.K.H.; software, S.M.K.H.; validation, S.M.K.H.; formal analysis, S.M.K.H. and C.A.L.; resources, S.M.K.H. and C.A.L.; writing--original draft preparation, S.M.K.H.; writing--review and editing, S.M.K.H. and C.A.L.; visualization, S.M.K.H.; supervision, C.A.L.; funding acquisition, C.A.L. All authors have read and agreed to the published version of the manuscript.  
Research reported in this publication was supported by the National Institute of General Medical Sciences Award No. R35 GM128877 of the National Institutes of Health, and the Office of Advanced Cyber infrastructure Award No. 1808530 of the National Science Foundation.  
The authors declare no conflict of interest.