MIA is an open-source standalone deep learning application for microscopic image analysis

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

“…The use of DL in oncology began with the analysis of medical images, because it is particularly good at identifying pathogenic features of the observed cells, and in certain cases the performance of DL is almost equal to human performance ( LeCun et al, 2015 ; Jalloul et al, 2023 ). For example, the application MIA was developed to analyze images from microscopy and can be used for classification, object recognition, segmentation, and tracking ( Körber, 2023 ). In addition, medical images of histopathological tumor sections were used to test whether DL can predict response to therapy in patients with adenocarcinoma of the gastroesophageal junction.…”

Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics

“…This has led to the development of high-throughput methods for classifying the status of cell cycle and identifying subcellular features using deep learning toolkits. [42][43][44][45][46][47] Even unlabeled transmitted-light images can be used to predict different fluorescence labels like cell nuclei and cell type. 48 Phase-contrast imaging can also be used computationally to perform live-dead assays on unlabeled cells.…”

BiaPy: A unified framework for versatile bioimage analysis with deep learning

“…These workflows encompass tasks ranging from semantic/instance segmentation to object detection, tracking, image classification, and reconstruction. To democratize these diverse workflows through deep learning, a range of solutions are available [3][4][5][6][7][8][9][10][11][12][13][14][15]. The integration of deepImageJ [4] brought pretrained deep learning models into Fiji [3], leveraging a diverse range of models accessible through the Bioimage Model Zoo [16].…”