| Dr. K. Murali Mohan Achari | |
| Affiliation | Assistant Professor,Department of Chemistry, Sri Venkateswara College, University of Delhi, New Delhi. |
| Email-Id: | mmakamsali@svc.ac.in |
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Daily Archives: August 6, 2026
Explainable Deep Learning for Automated Image-Based Disease Classification
Authors: Rabins Porwal
Abstract: — Deep convolutional networks now match or exceed specialist performance on several image-based diagnostic tasks, yet their adoption in clinical practice remains limited by a problem that accuracy alone cannot solve: a clinician asked to act on a prediction cannot see why it was made. Post-hoc saliency methods offer a partial answer, but different methods applied to the same network routinely disagree, and a map that looks convincing is not necessarily one that reflects the computation the model actually performed. This paper proposes an Explainable Deep Learning (XDL) framework in which interpretability is a training objective rather than an afterthought. An attention-guided refinement module reweights backbone feature maps so that spatial evidence is concentrated before classification. Three complementary attribution methods – Grad-CAM++, integrated gradients and GradientSHAP – are then fused into a single saliency map, and the disagreement among them is quantified as an explanation-consistency score. Finally, a deletion-based faithfulness penalty is added to the loss, so that the network is optimised not only to classify correctly but to concentrate its evidence on regions whose removal genuinely changes the prediction. Evaluation across five public datasets spanning radiography, dermoscopy, fundus photography, magnetic resonance imaging and histopathology gave a mean accuracy of 93.7 per cent, 2.1 percentage points above the strongest baseline. More importantly, faithfulness improved substantially: deletion AUC fell from 0.157 to 0.128 and agreement with expert-annotated lesion masks rose from 0.452 to 0.518 in intersection over union. A blinded review by three clinicians rated the fused maps 4.2 out of 5 for plausibility against 3.6 for the best competing method.
Explainable Reinforcement Learning for Real-Time Credit Risk Decisioning in Wholesale Banking Ecosystems
Authors: Sakthi Sankara Balaji Sathyamurthy
Abstract: Wholesale banking credit decisions involve large and interconnected exposures, evolving borrower conditions, collateral movements, covenant compliance, and changing market environments. Conventional credit-risk models are commonly designed for one-time prediction tasks and may not adequately support sequential decisions such as adjusting exposure limits, revising pricing, requesting additional collateral, or escalating cases for review. This study proposes an explainable reinforcement learning framework for real-time credit-risk decisioning in wholesale banking ecosystems. The framework represents credit management as a sequential decision problem in which an agent observes dynamic borrower, facility, portfolio, and market conditions before recommending appropriate risk actions. It integrates offline reinforcement learning with policy constraints to reduce the risk of unsafe or non-compliant recommendations in regulated banking environments. The proposed model incorporates an explainability layer that provides feature-based explanations, reward decomposition, policy rationale, and counterfactual action analysis. These outputs are intended to clarify why a specific credit action is recommended, which risk factors influenced the decision, how the recommendation aligns with risk appetite, and what changes in borrower conditions could lead to an alternative outcome. The study adopts a design science and empirical evaluation approach using historical wholesale credit data, with performance assessed against conventional statistical models, machine- learning models, rule-based decision engines, and non-explainable reinforcement-learning approaches. Evaluation criteria include credit-risk performance, expected-loss control, risk-adjusted return, decision latency, policy compliance, robustness, and explanation quality. The study contributes a structured framework for applying explainable reinforcement learning to high-impact credit decisions. It also provides guidance for controlled deployment through offline training, human oversight, model validation, audit logging, and continuous monitoring. The proposed approach may strengthen the timeliness, consistency, transparency, and governance of credit-risk decisioning across wholesale banking operations.