Reinforcement learning-based dynamic pruning for distributed inference via explainable AI in healthcare IoT systems

Deep Neural Networks (DNNs) have become the key technique to revolutionize the healthcare sector. However, conducting online remote inference is often impractical due to privacy constraints and latency requirements. To enable local computation, researchers have attempted network pruning with minimal accuracy loss or DNN distribution without affecting the performance. Yet, distributed inference can be inefficient due to the energy overhead and fluctuation of communication channels between participants. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and retraining relying only on the available scarce data is not possible. Even pre-pruned DNNs are limited in their ability to customize to the local load of data and device dynamics. The online pruning of DNN inferences without retraining is viable; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, we propose a novel pruning strategy using Explainable AI (XAI) to enhance the performance of pruned DNNs without retraining, a necessity due to the scarcity and bias of local healthcare data. We combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning when needed for highest accuracy. We use Non-Linear Integer Programming (NLP) to formulate our approach as a trade-off between resources and accuracy, and Reinforcement Learning (RL) to relax the problem and adapt to dynamic requirements. Our pruning criterion shows high performance compared to other reference techniques and ability to assist distribution by reducing resource usage while keeping high accuracy.Other Information Published in: Future Generation Computer Systems License: http://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://dx.doi.org/10.1016/j.future.2024.01.021</p

Additional file 1: of Predominance and association risk of Blastocystis hominis subtype I in colorectal cancer: a case control study

Raw results data of different patient groups. Description of data: The original numbers and information of different groups of investigated patients along with the raw data of the obtained results. (XLSX 50 kb

Additional file 2: Figure S2. of Activins and their related proteins in colon carcinogenesis: insights from early and advanced azoxymethane rat models of colon cancer

Steps of processing the study digital images with ImageJ software for calculating the surface areas of colonic adenoma (left column; ×100 magnification), carcinoma in situ (middle column; ×100 magnification) and adenocarcinoma (right column; ×200 magnification) observed by light microscopy and following staining with haematoxylin & eosin. The areas of interest were also selected with the support of an expert histopathologist (2nd row from top), then processed for colour threshold adjustment using HSB and ‘red’ as threshold colour (3rd row from top) and, finally all the images were transformed to binary colours in which the areas of interest appear in solid black colour (bottom row). All measurements were calculated following calibration with digital photos of corresponding microscopic scale slides captured at the designated magnifications. (Panels 1A-E and 2A-E: ×100 magnification, scale bar = 15 μm; panels 3A-E × 200 magnification, scale bar = 8 μm). (PPTX 1748 kb

Additional file 1: Figure S1. of Activins and their related proteins in colon carcinogenesis: insights from early and advanced azoxymethane rat models of colon cancer

Steps of processing the study digital images with ImageJ software for calculating the surface areas of colonic micro-tumours (left column) and flat ACF (middle column) detected by dissecting microscopy following methylene blue staining; and MDF (right column) by light microscopy following staining with 1 % Alcian blue. The identification and selection of the areas of interest (2nd row from top) were done with the guidance of an expert histopathologist. The images were then processed using hue/saturation/brightness (HSB) for colour threshold adjustment using ‘red’ as the threshold colour to digitally mark and select an area of interest by the software (3rd row from top). This was followed by transforming all the images to binary colours to ensure that only the areas of interest were precisely defined and selected by the software (bottom row). All measurements were calculated following calibration with digital photos of corresponding microscopic scale slides captured at the designated magnifications. (Panels 1A-E and 2A-E: ×20 magnification, scale bar = 2 mm; panels 3A-E: ×200 magnification, scale bar = 8 μm). (PPTX 1499 kb