Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading.

This paper presents a novel approach to enhance the accuracy of patch-level Gleason grading in prostate histopathology images, a critical task in the diagnosis and prognosis of prostate cancer. This study shows that the Gleason grading accuracy can be improved by addressing the prevalent issue of label inconsistencies in the SICAPv2 prostate dataset, which employs a majority voting scheme for patch-level labels. We propose a multi-label ensemble deep-learning classifier that effectively mitigates these inconsistencies and yields more accurate results than the state-of-the-art works. Specifically, our approach leverages the strengths of three different one-vs-all deep learning models in an ensemble to learn diverse features from the histopathology images to individually indicate the presence of one or more Gleason grades (G3, G4, and G5) in each patch. These deep learning models have been trained using transfer learning to fine-tune a variant of the ResNet18 CNN classifier chosen after an extensive ablation study. Experimental results demonstrate that our multi-label ensemble classifier significantly outperforms traditional single-label classifiers reported in the literature by at least 14% and 4% on accuracy and f1-score metrics respectively. These results underscore the potential of our proposed machine learning approach to improve the accuracy and consistency of prostate cancer grading.

Do Embedded and Peripheral Corporate Social Responsibility Activities Lower Employees' Turnover Intentions?

Corporate social responsibility (CSR) remains a topic of interest for both theory and practice due to its multifaceted avenues and potential for growth. We have chosen embedded CSR and peripheral CSR measures to evaluate how these activities affect the employee turnover intentions via a mediation mechanism of organizational citizenship behavior (OCB). In doing so, this study addresses important stakeholder concerns and provides meaningful managerial contributions for the employers to encourage more employee participation (through lowering turnover intention) toward sustainable corporate performance. This study incorporates four hypotheses that are tested in a structural equation modeling framework by employing Warp-PLS software. Data were collected from 297 employees working in firms that are renowned for their CSR initiatives. We found support for our key hypotheses leading to strong theoretical contributions to the stakeholder theory. We have addressed the main issues of stakeholders' response to the CSR tradeoffs and have tried to develop a deeper understanding of managers in initiating peripheral and embedded CSR activities for their firms.

ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection.

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include 'No Referable Diabetic Macular Edema Grade (DME)' and 'Referable DME' while five categories consist of 'Proliferative diabetic retinopathy', 'Severe', 'Moderate', 'Mild', and 'No diabetic retinopathy'. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.