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project_review


01. coordconv solution


https://arxiv.org/abs/1807.03247

idea

Alt text

  • coordconv : 좌표정보를 input에 concat 시켜 추가하고 conv 연산을 수행해 모델이 좌표정보 또한 학습하게끔 만듬
    • input에 nomalization한 좌표정보 i,j를 concat

01-1. AUTOMATED SEGMENTATION OF PULMONARY LOBES USING Coordination-Guided Deep Neural Networks


https://arxiv.org/abs/1904.09106

task & contributions

  • propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images

dataset

  • 343 chest CT scans
  • size : 256×256×128
  • target : five target lobar classes

model

Alt text

  • fully end-to-end 3D deep learning approach
  • unet 구조와 유사한데 큰 차이점은 skip connection 사용
  • coordconv 적용 : last transition in the decoding path

evaluation

Alt text


02. Focal loss


idea

Alt text

  • pt가 클 경우 상대적으로 loss가 pt가 적을 때 보다 크게 감소
  • 따라서 상대적으로 잘 분류되지 못한 cls에 집중함

02-1. A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTION U-NET FOR LESION SEGMENTATION


https://arxiv.org/abs/1810.07842

task & contributions

  • a novel focal Tversky loss function for highly imbalanced data and small ROI segmentation
  • a deeply supervised attention U-Net improved with a multiscaled input image pyramid for better intermediate feature representations.

dataset

  • 1.Breast Ultrasound Lesions 2017 dataset B (BUS)

    • 163 ultrasound images of breast lesions from different women
    • average image size is 760 x 570 pixels(resampled to 128 x 128 pixels)
    • a 75-25 train-test split
  • 2.ISIC 2018 skin lesion dataset

    • 2,594 RGB images of skin lesion
    • image size of 2166 x 3188 pixels(resampled to 192 x 256 pixels)
    • 75-25 train-test split

focal Tversky loss function (FTL)

  • The Tversky index is adapted to a loss function (TL)

Alt text

* pic : probability that pixel i is of the lesion class c
* pic-: probability pixel i is of the non-lesion class c¯
* same is true for gic and gic¯
*  α,β 는 하이퍼파라미터

Alt text

  • focal Tversky loss function (FTL)

    Alt text

  • γ : 파라미터[1~3] /best : 4/3

model

  • attention gate
  • multi scale : avg pooling을 이용해 각 stage의 input으로 추가로 넣어줌
  • deep supervision

evaluation

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