For esophageal cancer immunotherapy, Programmed Death Ligand-l (PD-LI) is considered a predictive biomarker. However, immunehistochemistry (IHC) methods used to quantify PD-LI are challenged by high cost, time and variability. In contrast, hematoxylin and eosin (H&E) staining is a reliable method commonly used in cancer diagnosis. By employing advanced deep learning techniques, this study demonstrates the feasibility of predicting PD-LI expression from H&E stained images. With the help of pathologists, a dataset is constructed to evaluate the validity of PD-LI prediction in esophageal cancer by H&E using the FusedNet model. In 227 patients, PD-LI status is systematically predicted. Consistent prediction performance is demonstrated through validation of the validation set, proving that the system can be used as a decision support and quality assurance system in clinical practice.

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network.