Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

• URL: http://arxiv.org/abs/2208.12437v1
• Date: Fri, 26 Aug 2022 04:59:43 GMT
• Title: Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge
• Abstract summary: This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN) Our approach consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we then calculate each extracted patch's class activation map to obtain the mitosis's precise location. To increase the model generalizability, we train the CNN with a series of data augmentation techniques, a loss that copes with noise-labeled images, and an active learning strategy.
• Score: 4.593314647169437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our approach consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we then calculate each extracted patch's class activation map to obtain the mitosis's precise location. To increase the model generalizability, we train the CNN with a series of data augmentation techniques, a loss that copes with noise-labeled images, and an active learning strategy. Our approach achieved an F1 score of 0.7323 with an EfficientNet-b3 model in the preliminary test phase of the MIDOG 2022 challenge.

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