An effective approach to the quantification of patchiness in biomedical images according to their local edge densities.

A **Style-based Recalibration Module (SRM)** is a module for convolutional neural networks that adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight via channel-independent style integration. By incorporating the relative importance of individual styles into feature maps, SRM is aimed at enhancing the representational ability of a CNN.

The overall structure of SRM is illustrated in the Figure to the right. It consists of two main components: style pooling and style integration. The style pooling operator extracts style features
from each channel by summarizing feature responses across spatial dimensions. It is followed by the style integration operator, which produces example-specific style weights by utilizing the style features via channel-wise operation. The style weights finally recalibrate the feature maps to either
emphasize or suppress their information.

Style-based Recalibration Module

SRM : A Style-based Recalibration Module for Convolutional Neural Networks

PALED

Segmentation of patchy areas in biomedical images based on local edge density estimation

A new kind of implicit models, where the output of the network is defined as the solution to an "infinite-level" fixed point equation. Thanks to this we can compute the gradient of the output without activations and therefore with a significantly reduced memory footprint.

Source	Segmentation of patchy areas in biomedical images based on local edge density estimation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)