MULTIPLE INSTANCE LEARNING WITH TRANSFORMERS FOR DIABETIC RETINOPATHY DIAGNOSIS

The process for diagnosing diabetic retinopathy using multiple instance learning is demonstrated in this video. It starts with preprocessing the input data through an adaptive bilateral filter to enhance features. Subsequently, fuzzy C-means clustering and a Gabor filter are applied to extract relevant information. Finally, the processed data is fed into a Deep Vision Net for prediction, enabling accurate diagnosis of the condition.

Видео MULTIPLE INSTANCE LEARNING WITH TRANSFORMERS FOR DIABETIC RETINOPATHY DIAGNOSIS канала AB TECHNOLOGIES