vandana Panwar

This story first appeared in the May/June issue of SUPPLY CHAIN XCHANGE, a journal of thought leadership for the supply chain management profession and a sister publication to AGiLE Business Media & Events’ DC VELOCITY. In an era of escalating trade tensions and shifting global dynamics, businesses are grappling with the ripple effects of tariffs on their supply chains. Companies are scrambling to scenario plan and adapt their strategies to maintain competitiveness and profitability.

Abstract Deep learning (DL) algorithms have demonstrated remarkable proficiency in histopathology classification tasks, presenting an opportunity to discover disease [ndash]related features escaping visual inspection. However, the ″black box″ nature of DL obfuscates the basis of the classification. Here, we develop an algorithm for interpretable Deep Learning (IDL) that sheds light on the links between tissue morphology and cancer biology.

Abstract Deep learning (DL) algorithms have demonstrated remarkable proficiency in histopathology classification tasks, presenting an opportunity to discover disease [ndash]related features escaping visual inspection. However, the ″black box″ nature of DL obfuscates the basis of the classification. Here, we develop an algorithm for interpretable Deep Learning (IDL) that sheds light on the links between tissue morphology and cancer biology.