Mulham91

ALGORITHMIC-BIAS-FAIRNESS-AND-ETHICS

data

Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a1performance analysis of different deep learning based segmentation methods for automatic lesion2segmentation on oral carcinoma images. Two diverse image datasets are considered for training3and testing the convolutional networks used for segmenting the images, thus allowing to obtain4an evaluation of the generalization capability of the considered deep learning architectures. An5important contribution of this work is the creation of the ORal Cancer Annotated (ORCA) dataset,6containing about 900 annotated images derived from the well-known Cancer Genome Atlas (TCGA)7datase

fawakherji-diag.uniroma1.it

Sunflower_Spade_Dataset

guided_train

mixed_sunflower

model

Multi-Spectral-Image-Synthesis-for-Crop-Weed-Segmentation-in-Precision-Farming

In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.

new_weed_dataset

object_detection

Mulham91/Multi-Spectral-Image-Synthesis-for-Crop-Weed-Segmentation-in-Precision-Farming

In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.

Mulham91/ALGORITHMIC-BIAS-FAIRNESS-AND-ETHICS

Mulham91/Deep-Learning-based-Pixel-wise-Lesion-Segmentationon-Oral-Squamous-Cell-Carcinoma-Images

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a1performance analysis of different deep learning based segmentation methods for automatic lesion2segmentation on oral carcinoma images. Two diverse image datasets are considered for training3and testing the convolutional networks used for segmenting the images, thus allowing to obtain4an evaluation of the generalization capability of the considered deep learning architectures. An5important contribution of this work is the creation of the ORal Cancer Annotated (ORCA) dataset,6containing about 900 annotated images derived from the well-known Cancer Genome Atlas (TCGA)7datase

Mulham91/data

Mulham91/fawakherji-diag.uniroma1.it

Sunflower_Spade_Dataset

Mulham91/guided_train

Mulham91/mixed_sunflower

Mulham91/model

Mulham91/new_weed_dataset

Mulham91/object_detection

Mulham91/sugarbeet_dataset

Mulham91/sunflowe_synthetic_crop

Mulham91/test