Pathology Language and Image Pre-Training (PLIP) is a vision-and-language foundation model created by fine-tuning CLIP on pathology images.

An **Inverted Residual Block**, sometimes called an **MBConv Block**, is a type of residual block used for image models that uses an inverted structure for efficiency reasons. It was originally proposed for the [MobileNetV2](https://paperswithcode.com/method/mobilenetv2) CNN architecture. It has since been reused for several mobile-optimized CNNs.

A traditional [Residual Block](https://paperswithcode.com/method/residual-block) has a wide -> narrow -> wide structure with the number of channels. The input has a high number of channels, which are compressed with a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution). The number of channels is then increased again with a 1x1 [convolution](https://paperswithcode.com/method/convolution) so input and output can be added. 

In contrast, an Inverted Residual Block follows a narrow -> wide -> narrow approach, hence the inversion. We first widen with a 1x1 convolution, then use a 3x3 [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) (which greatly reduces the number of parameters), then we use a 1x1 convolution to reduce the number of channels so input and output can be added.

Inverted Residual Block

MobileNetV2: Inverted Residuals and Linear Bottlenecks

PLIP

Leveraging medical Twitter to build a visual–language foundation model for pathology AI

**Semantic reasoning network**, or **SRN**, is an end-to-end trainable framework for scene text recognition that consists of four parts: backbone network, parallel [visual attention](https://paperswithcode.com/method/visual-attention) module (PVAM), global semantic reasoning module (GSRM), and visual-semantic fusion decoder (VSFD). Given an input image, the backbone network is first used to extract 2D features $V$. Then, the PVAM is used to generate $N$ aligned 1-D features $G$, where each feature corresponds to a character in the text and captures the aligned visual information. These $N$ 1-D features $G$ are then fed into a GSRM to capture the semantic information $S$. Finally, the aligned visual features $G$ and the semantic information $S$ are fused by the VSFD to predict $N$ characters. For text string shorter than $N$, ’EOS’ are padded.

Source	Leveraging medical Twitter to build a visual–language foundation model for pathology AI
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com