Medical Imaging

nnU-Net is now considered a standard and state-of-the-art tool for medical image segmentation, but, I think that it is opinionated in the following ways that affect how I may use it: It enforces a specific training and evaluation workflow, including assumptions about data splits. This means using custom train-valid-test splits requires some workarounds. It assumes 3D volumetric data, and while 2D data is supported, it’s the not the primary use case. It assumes grayscale images, and does not support RGB images out of the box. This again means that RGB images require some workarounds. A lot of researchers, however, happily use nnU-Net for 3D volumetric medical images, so these limitations may not be relevant to them. ...

The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...

In this paper, the authors proposed a fully convolutional neural network architecture for biomedical image segmentation which overcame the limitations of the contemporary algorithms. Unlike other popular algorithms then, the proposed network did not suffer from the redundancy arising out of overlapping training patches. Moreover, the authors eliminate the trade-off between localization accuracy and the use of context and state that “good localization and the use of context are possible at the same time”. ...

As the title suggests, this paper studies various different deep convolutional neural network architectures and various techniques to use these CNNs for CADe (Computer Aided Detection) tasks. With the tremendous popularity of CNN models, the authors state that the “tremendous” success of CNNs in medical image tasks has been primarily using three techniques. ...