Gravity Network for end-to-end small lesion detection
This addresses the challenge of precise localization of small lesions in medical imaging, which is crucial for early diagnosis, but it appears incremental as it builds on existing detection frameworks with a novel anchor mechanism.
The paper tackles the problem of detecting small lesions in medical images by introducing GravityNet, a one-stage end-to-end detector with pixel-based anchors that dynamically move towards lesions, achieving promising results in experiments on microcalcifications in mammograms and microaneurysms in fundus images.
This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.