Tirtha Chanda
A5053631086- Cutaneous Melanoma Detection and Management
- Explainable Artificial Intelligence (XAI)
- AI in cancer detection
- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
- Topic Modeling
German Cancer Research Center
2024-2025
Heidelberg University
2024-2025
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need objective evaluation of how dermatologists engage both XAI tools. In this study, 76 participated reader diagnosing 16 dermoscopic images melanomas nevi using an system that provides detailed, domain-specific explanations....
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using Gleason scoring system. While artificial intelligence (AI) has shown promise accurately predicting scores, these predictions often lack inherent explainability, potentially leading to distrust human-machine interactions. To address this issue, we introduce a novel dataset 1,015 tissue microarray core images, annotated by an international group 54...
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, lack transparency in how these identify poses severe obstacles user acceptance. Explainable (XAI) methods can help increase transparency, but most XAI are unable produce precisely located domain-specific explanations, making explanations difficult interpret. Moreover, impact on dermatologists has not yet evaluated. Extending two existing classifiers, we developed an system...