Deep Learning – Image Segmentation
Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
Objective
Colorectal cancer (CRC) usually begins as a polyp in the intestinal mucosa, and approximately one quarter of untreated polyps can develop into colon cancer.
As the polyps are usually small and the boundaries are low in contrast to their surroundings, polyps can easily be mistaken for wrinkles or other intestinal structures.
Polyp detection using colonoscopy images is a challenging task owing to the ambiguous image context.
Data
Train & Validation: Kvasir (900 images), CVC-ClinicDB (550)
Test: Kvasir (100), CVC-ClinicDB(62), CVC-ColonDB (380), ETIS (196), EndoScene.CVC-300 (60)
CVC-Clinic – https://polyp.grand-challenge.org/site/Polyp/CVCClinicDB/
CVC-Colon – http://www.cvc.uab.es/CVC-Colon/index.php/databases/
ETIS, EndoScene – http://www.cvc.uab.es/CVC-Colon/index.php/databases/cvc-endoscenestill/
Related Work
U-Net and U-Net++ exhibits a distribution discrepancy between the low-level and high-level representations when aggregating multi-level features.
Psi-Net and SFA addressed a joint training strategy using the polyp region and boundary detection tasks.
PraNet employed a parallel reverse attention method with partial decoders to incorporate the polyp area and boundary features.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
SFA: Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Proposed Method
The proposed network, COMMA, is designed to reduce the multi-level distribution discrepancy by propagating both refined levels and explicit boundary information. To proliferate distinct information, we employ multi-decoder structures consisting of CMMs and BPM.
CMM : CMM clarifies the boundary noise in the low-level through the abstracted high-level representation and propagates the refined information to another decoder.
BPM : BPM is designed to propagate the explicit boundary information to the complementary multi-level features by incorporating the lowest- and highest-level representations. The boundary information is propagated to the CMMs in the next decoder to enhance the segmentation performance.
얼굴 피부 내 Object Segmentation
얼굴 피부 내 작은 객체들(pore, pigment, sebum, trouble 등)을 segmentation함에 있어, Radiance Noise의 영향을 최소화하여 재현성 높은 결과를 산출할 수 있는 학습 모델을 개발함.
(1) Prototype App을 통해 150명의 피부 데이터를 수집 후, crop → tiling → augmentation 전처리하여 약 4000장의 이미지를 모델에 인풋 (augmentation에 random bright&contras를 적용하여 radiance noise를 줄이고자 함)
(2) 객체 크기가 매우 작기 때문에 SOTA 모델 중 하나인 Mask R-CNN 모델을 적용하여 RoI Align 기반의 정확한 위치 정보를 학습하고자 함 (가장 작은 object 크기 2~4px)
(3) 보다 높은 성능을 위해 추후 Graph based Approach 및 Vision Transformer 등의 모델을 fine-tunin하여 적용하여 최종 성능을 비교 평가하고자 함.