prresearch
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Indifference Subspace of Deep Features for Lung Nodule Classification from CT Images
This work examines whether standard DL networks can produce common feature node magnitudes for same-class images. Surprisingly, the indifference subspace that emerges from standard DL networks performs remarkably, allowing fine-tuning with CVA and yielding classification performances on par with state-of-the-art… Continue reading
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Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?
The unfair model often incorrectly focused on non-tumor areas for malignancy predictions, while the fair model correctly concentrated on tumor regions. This indicates the unfair model’s unreliability, likely stemming from overfitting due to improper train/test splitting. Continue reading
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Neural Network Representations for the Inter- and Intra-Class Common Vector Classifiers
In this work, we explore this schematic similarity to come up with an ANN representation for both CVA and DCVA. The new representation eliminates the need for projection matrices in its implementation, hence significantly reduces the memory requirements and computational… Continue reading
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Pattern Recognition Research Lab.
We are a group of researchers from different universities, applying our ML & AI knowledge to real-life pattern recognition problems. Continue reading
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Deep Learning Enhancement of Low-Dose CT Images for Improved Diagnostic Accuracy
The use of ionizing radiation in diagnostic imaging is a common practice worldwide. However, the imaging process itself carries a relative risk. Therefore, it is recommended to employ the lowest possible dose of ionizing radiation, especially in computed tomography (CT)… Continue reading
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Necessary Conditions for Successful Application of Intra- and Inter-class Common Vector Classifiers
The study investigates the performance of two Common Vector Approach (CVA) variations, mutual CVA, and Discriminative CVA (DCVA) in image classification. It points to two risks associated with DCVA, particularly when classifying binary images. CVA, handled with care, outperforms DCVA… Continue reading
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Use of gradient and normal vectors for face recognition
Use of gradient and normal vectors for face recognition The main objective of this study is to compare face recognition accuracies in the case when the grey levels in each pixel of the face images are replaced by the gradient… Continue reading
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Two Pseudo-Common Vectors for Pattern Recognition
Two Pseudo-Common Vectors for Pattern Recognition In this paper, the mathematical model used in finding the common vectors of classes in pattern recognition problems is reconsidered to obtain possible alternative solutions for the common vectors. Since the number of unknowns… Continue reading
Pattern Recognition Research Lab. 