Abstract: The present disclosure provides method and system 100 for classifying defects in wafer using wafer defect images, based on deep learning network. Embodiments herein uses synergy between several modalities of the wafer defect images for the classification decision. Further, by adding a mixture of modalities, information may be obtained from different sources such as color image, ICI, the black and white image, to classify the defect image. In addition to mixture of modalities, a reference image may be used for each modality. The reference image of each modality image is provided to deep learning models to concentrate on the defect itself and not on the related underlying lithography of the defect image. Further, the reference image may be provided to the training process of the deep learning models that may significantly reduce the number of labelled images and the training epochs required for convergence of the deep learning model.

Abstract: The invention relates to an automated decision support system, method, and apparatus for analysis and detection of bacteria in histological sections from tissue biopsies in general, and more specifically, of Helicobacter pylori (HP) in histological sections from gastric biopsies. The method includes image acquisition apparatus, data processing and support system conclusions, methods of transferring and storing the slide data, and pathologist diagnosis by reviewing and approving the images classified as containing bacteria, and more specifically HP findings.

Abstract: The invention relates to an automated decision support system, method, and apparatus for analysis and detection of bacteria in histological sections from tissue biopsies in general, and more specifically, of Helicobacter pylori (HP) in histological sections from gastric biopsies. The method includes image acquisition apparatus, data processing and support system conclusions, methods of transferring and storing the slide data, and pathologist diagnosis by reviewing and approving the images classified as containing bacteria, and more specifically HP findings.

Abstract: The invention relates to an automated decision support system, method, and apparatus for analysis and detection of bacteria in histological sections from tissue biopsies in general, and more specifically, of Helicobacter pylori (HP) in histological sections from gastric biopsies. The method includes image acquisition apparatus, data processing and support system conclusions, methods of transferring and storing the slide data, and pathologist diagnosis by reviewing and approving the images classified as containing bacteria, and more specifically HP findings.