Efficient multi-stage feedback attention for diverse lesion in cancer image segmentation

In the domain of Computer-Aided Diagnosis (CAD) systems, the accurate identification of cancer lesions is paramount, given the life-threatening nature of cancer and the complexities inherent in its manifestation. This task is particularly arduous due to the often vague boundaries of cancerous regions, compounded by the presence of noise and the heterogeneity in the appearance of lesions, making precise segmentation a critical yet challenging endeavor. This study introduces an innovative, an iterative feedback mechanism tailored for the nuanced detection of cancer lesions in a variety of medical imaging modalities, offering a refining phase to adjust detection results. The core of our approach is the elimination of the need for an initial segmentation mask, a common limitation in iterative-based segmentation methods. Instead, we utilize a novel system where the feedback for refining segmentation is derived directly from the encoder–decoder architecture of our neural network model. This shift allows for more dynamic and accurate lesion identification. To further enhance the accuracy of our CAD system, we employ a multi-scale feedback attention mechanism to guide and refine predicted mask subsequent iterations. In parallel, we introduce a sophisticated weighted feedback loss function. This function synergistically combines global and iteration-specific loss considerations, thereby refining parameter estimation and improving the overall precision of the segmentation. We conducted comprehensive experiments across three distinct categories of medical imaging: colonoscopy, ultrasonography, and dermoscopic images. The experimental results demonstrate that our method not only competes favorably with but also surpasses current state-of-the-art methods in various scenarios, including both standard and challenging out-of-domain tasks. This evidences the robustness and versatility of our approach in accurately identifying cancer lesions across a spectrum of medical imaging contexts. Our source code can be found at https://github.com/dewamsa/EfficientFeedbackNetwork.