RethNet: Object-by-Object Learning for Detecting Facial Skin Problems

TL;DR

RethNet introduces an object-by-object learning approach with contextual attention mechanisms to improve semantic segmentation of facial skin lesions, achieving significant accuracy gains over existing methods.

Contribution

The paper presents RethNet, a novel model with REthinker blocks that incorporate convLSTM/Conv3D and SE modules for enhanced local and global context understanding in skin lesion detection.

Findings

Achieved MIoU of 79.46% on skin lesion dataset

Outperformed Deeplab v3+ by 15.34% in MIoU

Effectively captures co-occurrence relations between skin lesion types

Abstract

Semantic segmentation is a hot topic in computer vision where the most challenging tasks of object detection and recognition have been handling by the success of semantic segmentation approaches. We propose a concept of object-by-object learning technique to detect 11 types of facial skin lesions using semantic segmentation methods. Detecting individual skin lesion in a dense group is a challenging task, because of ambiguities in the appearance of the visual data. We observe that there exist co-occurrent visual relations between object classes (e.g., wrinkle and age spot, or papule and whitehead, etc.). In fact, rich contextual information significantly helps to handle the issue. Therefore, we propose REthinker blocks that are composed of the locally constructed convLSTM/Conv3D layers and SE module as a one-shot attention mechanism whose responsibility is to increase network's sensitivity in the local and global contextual representation that supports to capture ambiguously appeared objects and co-occurrence interactions between object classes. Experiments show that our proposed model reached MIoU of 79.46% on the test of a prepared dataset, representing a 15.34% improvement over Deeplab v3+ (MIoU of 64.12%).