
- Read more about Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging
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The remarkable performance of deep neural networks (DNNs) currently makes them the method of choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images. In these applications, DNNs invert a forward operator by finding, via training data, a map between the measurements and the input images. It is then expected that the map is still valid for the test data. This framework, however, introduces measurement inconsistency during testing.
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- Read more about CMIM: CROSS-MODAL INFORMATION MAXIMIZATION FOR MEDICAL IMAGING
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- Read more about Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning
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Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging fields, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has specifically analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classification.
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- Read more about Instance segmentation with the number of clusters incorporated in embedding learning
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Poster.pdf

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- Read more about SEMI-SUPERVISED SKIN LESION SEGMENTATION WITH LEARNING MODEL CONFIDENCE
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- Read more about SEMI-SUPERVISED SKIN LESION SEGMENTATION WITH LEARNING MODEL CONFIDENCE
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- Read more about SEA-NET: SQUEEZE-AND-EXCITATION ATTENTION NET FOR DIABETIC RETINOPATHY GRADING
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Diabetes is one of the most common disease in individuals. Diabetic retinopathy (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods.
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