**Co-Correcting** is a noise-tolerant deep learning framework for medical image classification based on mutual learning and annotation correction. It consists of three modules: the dual-network architecture, the curriculum learning module, and the label correction module.

The **Bottleneck Transformer (BoTNet) ** is an image classification model that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a [ResNet](https://paperswithcode.com/method/resnet) and no other changes, the approach improves upon baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency.

Bottleneck Transformer

Bottleneck Transformers for Visual Recognition

Co-Correcting

Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning.  PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)

Source	Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com