Self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

**STDC**, or **Short-Term Dense Concatenate**, is a module for semantic segmentation to extract deep features with scalable
receptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be time-consuming,. Instead, STDC gradually reduces the dimension of feature maps and use the aggregation of them for image representation.

We concatenate response maps from multiple continuous layers, each of which encodes input image/feature in different scales and respective fields, leading to multi-scale feature representation. To speed up, the filter size of layers is gradually reduced with negligible loss in segmentation performance.

STDC

Rethinking BiSeNet For Real-time Semantic Segmentation

Magnification Prior Contrastive Similarity

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

**RepVGG** is a [VGG](https://paperswithcode.com/method/vgg)-style convolutional architecture. It has the following advantages:

- The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes
the output of its only preceding layer as input and feeds the output into its only following layer.
- The model’s body uses only 3 × 3 conv and [ReLU](https://paperswithcode.com/method/relu).
- The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic
search, manual refinement, compound scaling, nor other heavy designs.

Source	Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
Year	2000
Data Source	Papers with Code archive (CC BY-SA)