Pinned Repositories
End-to-End-LLM
This repository is an AI Bootcamp material that consist of a workflow for LLM
hover_net
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
S_CellDetect_Stardist
Based on https://github.com/cytomine/S_CellDetect_Stardist_HE_ROI but with modifications for used in other types of stains
S_CellDetect_Stardist_SISH_ROI
Cell (Nuclei) Detection in ROI using Stardist (mod)
S_CerviCare_Class
Classification of pap smear to Normal and Abnormal (Cervical Cancer) using DenseNet21 (Pytorch)
S_CerviCare_Class_OV
Classification of pap smear to Normal and Abnormal (Cervical Cancer) using DenseNet21 (Pytorch) with OpenVino optimisation.
S_classify_NWMS_ER_DenseNet201
Classification of cells to negative and positive (weak, moderate, strong) using Tensorflow-Keras DenseNet201
S_classify_NWMS_ER_DenseNet201_v3
S_classify_NWMS_ER_pytorch_DenseNet
Classification of cells to negative or positive of weak, moderate or strong using pytorch densenet
S_classify_NWMS_ER_pytorch_DenseNet201
Classification of cells to negative or positive of weak, moderate or strong using pytorch densenet201
mizjaggy18's Repositories
mizjaggy18/End-to-End-LLM
This repository is an AI Bootcamp material that consist of a workflow for LLM
mizjaggy18/S_CellDetect_Stardist
Based on https://github.com/cytomine/S_CellDetect_Stardist_HE_ROI but with modifications for used in other types of stains
mizjaggy18/S_CellDetect_Stardist_SISH_ROI
Cell (Nuclei) Detection in ROI using Stardist (mod)
mizjaggy18/S_CerviCare_Class
Classification of pap smear to Normal and Abnormal (Cervical Cancer) using DenseNet21 (Pytorch)
mizjaggy18/S_CerviCare_Class_OV
Classification of pap smear to Normal and Abnormal (Cervical Cancer) using DenseNet21 (Pytorch) with OpenVino optimisation.
mizjaggy18/S_classify_NWMS_ER_pytorch_DenseNet
Classification of cells to negative or positive of weak, moderate or strong using pytorch densenet
mizjaggy18/S_classify_NWMS_ER_pytorch_DenseNet201
Classification of cells to negative or positive of weak, moderate or strong using pytorch densenet201
mizjaggy18/S_classify_PN_cells
Classification of cells to positive or negative based on https://doi.org/10.1007/978-3-319-19156-0_17
mizjaggy18/S_classify_PN_DenseNet
Classification of nucleus to positive or negative and followed by Negative, Weak, Moderate and Strong for class positive using DenseNet.
mizjaggy18/S_Delete_selected_annotations
To delete manual annotations based on selected terms from image list
mizjaggy18/S_Delete_smallpatch
mizjaggy18/S_Delete_whitepatch
To delete patches with predefined white-bin threshold from 16-bin histogram
mizjaggy18/S_Download_Allred_Score
To download Allred score and classification details from NWMS classification terms
mizjaggy18/S_Download_analysis_annotations
To download annotations details from job (algorithm) analysis
mizjaggy18/S_Download_annotations_image
To download annotations image either from job (algorithm) analysis or manual
mizjaggy18/S_Download_manual_annotations
To download manual annotations details from image list
mizjaggy18/S_download_ROI_mask
Download ROI mask from nuclei annotations (all or reviewed only)
mizjaggy18/S_Get_region_coord
To get region coordinates from images of other projects
mizjaggy18/S_NPC_classifyDenseNet21
The model was trained to classify nasopharyngeal cases (Normal, LHP, NPI, NPC).
mizjaggy18/S_NPC_classifySimCLR
To classify NPC cases using SimCLR model
mizjaggy18/S_PC_Stardist_Class
Segmentation of nuclei using Stardist and Classification of nucleus to Others, Necrotic or Tumor (Pancreatic Cancer)
mizjaggy18/S_PDAC_3class
Classification for PDAC biopsy
mizjaggy18/S_ROI_splitpoly
To split a large region of interest in a whole-slide image to small polygon with user-defined sides in pixel.
mizjaggy18/S_Segment-Otsu
WSI threshold to segment tissue region using Otsu thresholding from skimage.filters.
mizjaggy18/S_Segment-Otsu-ROI
ROI-based threshold on WSI to segment tissue region using Otsu thresholding from skimage.filters.
mizjaggy18/S_splitROI_classifyDenseNet21
To split large ROI in WSI to small polygon with user-defined sides followed by DN21 classification. The model was trained to classify nasopharyngeal cases (Normal, LHP, NPI, NPC).
mizjaggy18/S_Stardist_DenseNet
Segmentation of nuclei using Stardist and Classification of nucleus to Negative, Weak, Moderate and Strong using DenseNet
mizjaggy18/S_Stardist_PN_DenseNet
Segmentation of nuclei using Stardist and Classification of nucleus to positive or negative and followed by Negative, Weak, Moderate and Strong for class positive using DenseNet
mizjaggy18/S_Thyroid_Class
Classification of MGG smear to c0-non-thyroid, c1-benign, and c2-malignant (Thyroid Cancer) using DenseNet21
mizjaggy18/TransPath