
# PANDA_ConvNeXT_K

An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input


ISUP 0:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c02d3bb3a62519b31c63d0301c6843e_0.jpeg">

ISUP 1:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0cee71ab57422e04f76e09ef2186fcd5_1.jpeg">
ISUP 2:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg">
ISUP 3:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c5c2d16c0f2e399b7be641e7e7f66d9_3.jpeg">
ISUP 4:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c88d7c7033e2048b1068e208b105270_4.jpeg">
ISUP 5:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00c15b23b30a5ba061358d9641118904_5.jpeg">
{"tags": ["image-classification", "pytorch"], "metrics": ["accuracy", "Cohen's Kappa"]}
transformers pytorch convnext image-classification model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 


# PANDA_ConvNeXT_K

An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input


ISUP 0:
<img width="256" height="256" src="URL

ISUP 1:
<img width="256" height="256" src="URL
ISUP 2:
<img width="256" height="256" src="URL
ISUP 3:
<img width="256" height="256" src="URL
ISUP 4:
<img width="256" height="256" src="URL
ISUP 5:
<img width="256" height="256" src="URL
# PANDA_ConvNeXT_K

An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input


ISUP 0:
<img width="256" height="256" src="URL

ISUP 1:
<img width="256" height="256" src="URL
ISUP 2:
<img width="256" height="256" src="URL
ISUP 3:
<img width="256" height="256" src="URL
ISUP 4:
<img width="256" height="256" src="URL
ISUP 5:
<img width="256" height="256" src="URL
TAGS
#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # PANDA_ConvNeXT_K

An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input


ISUP 0:
<img width="256" height="256" src="URL

ISUP 1:
<img width="256" height="256" src="URL
ISUP 2:
<img width="256" height="256" src="URL
ISUP 3:
<img width="256" height="256" src="URL
ISUP 4:
<img width="256" height="256" src="URL
ISUP 5:
<img width="256" height="256" src="URL
passage: TAGS
#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 
# PANDA_ConvNeXT_K

An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input


ISUP 0:
<img width="256" height="256" src="URL

ISUP 1:
<img width="256" height="256" src="URL
ISUP 2:
<img width="256" height="256" src="URL
ISUP 3:
<img width="256" height="256" src="URL
ISUP 4:
<img width="256" height="256" src="URL
ISUP 5:
<img width="256" height="256" src="URL