Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medical objects, analyzing small areas in medical images remains challenging. This difficulty arises due to information losses and compression defects from convolutional and pooling operations in CNNs, which become more pronounced as the network deepens, especially for small medical objects. To address…

• TR
  The Research Council

  Awards: ECS 21212720, GRF 11217922, 11212321