Singh, Richa

Deep learning models have demonstrated significant effectiveness in addressing intricate object segmentation and image classification tasks. Nevertheless, their widespread use is impeded by high computational demands, limiting their applicability on resource-constrained devices and in contexts like medical image segmentation. This paper proposes AssistDistil, a semi-knowledge distillation technique designed to facilitate the transfer of knowledge from a larger teacher network to a more compact student model. During the inference process, the student model works in conjunction with the teacher model by condensing the teacher model's latent information into its own latent representation, thereby boosting its representational capacity. The effectiveness of the proposed approach is demonstrated for multiple case studies in medical image segmentation task of eye segmentation, skin lesion segmentation, and chest X-ray segmentation. Experimental results on the IIITD Cataract Surgery, HAM10000, PH2, Shenzhen and Montgomery chest X-ray datasets demonstrate the efficacy of the proposed approach both in terms of accuracy and computational cost. For example, in comparison to the AUNet-based teacher model, the proposed approach achieves a similar mIOU with only 0.5% of the model size. In the future, we plan to explore knowledge distillation approaches to improve the distillation process in case of large model capacity gap between teacher and student networks. With fewer parameters, we intend for the student model to attain performance comparable to that of the teacher model without additional assistance.

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains, catalysing unparalleled enhancements across a broad spectrum of tasks; however, its integrity and trustworthiness have emerged as notable concerns. The scientific community has focused on the development of trustworthy AI algorithms; however, machine learning and deep learning algorithms, popular in the AI community today, intrinsically rely on the quality of their training data. These algorithms are designed to detect patterns within the data, thereby learning the intended behavioural objectives. Any inadequacy in the data has the potential to translate directly into algorithms. In this study we discuss the importance of responsible machine learning datasets through the lens of fairness, privacy and regulatory compliance, and present a large audit of computer vision datasets. Despite the ubiquity of fairness and privacy challenges across diverse data domains, current regulatory frameworks primarily address human-centric data concerns. We therefore focus our discussion on biometric and healthcare datasets, although the principles we outline are broadly applicable across various domains. The audit is conducted through evaluation of the proposed responsible rubric. After surveying over 100 datasets, our detailed analysis of 60 distinct datasets highlights a universal susceptibility to fairness, privacy and regulatory compliance issues. This finding emphasizes the urgent need for revising dataset creation methodologies within the scientific community, especially in light of global advancements in data protection legislation. We assert that our study is critically relevant in the contemporary AI context, offering insights and recommendations that are both timely and essential for the ongoing evolution of AI technologies.