Project Link Medical imaging is indispensable for diagnosis, treatment planning, and patient care, allowing clinicians to visualize internal structures and detect anomalies.
Despite technological advancements, accurately identifying abnormalities in MRI scans remains challenging due to complexity and variability.
Our goal is to empower healthcare professionals with timely and interpretable insights, enhancing diagnostic accuracy, optimizing treatment strategies, and ultimately improving patient outcomes.
RL has the potential to overcome the limitations of traditional supervised learning approaches, which often require large annotated datasets and may struggle with complex, ambiguous, or rare medical imaging patterns.
RL-based methods can learn to perform these tasks directly from image data, without the need for extensive feature engineering or manual annotation.
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