Optimizing Conformal Prediction Sets for Pathological Image Classification

• Authors: Shubham Ojha*, Aditya Narendra*, Abhay Kshirsagar, Shyam Sundar Debsarkar & Surya Prasath

• Venue : Under Preparation

Abstract

The intersection of Deep Learning (DL) and pathology has gained significant attention, encompassing cell clas- sification, detection, segmentation, and whole-slide image (WSI) analysis. Further works at this intersection have in- creasingly focused on integrating uncertainty quantification (UQ) with DL methods for pathology to address their occa- sional unreliability in clinical settings. Conformal Predic- tion (CP) is one of the UQ methods deployed for various medical settings including pathology. CP methods are com- putationally efficient and offer user-defined coverage guar- antees to generate prediction sets that include the true label. However, CP methods lack inherent control over the com- positionality of prediction sets, which restricts their clinical utility. This study presents a novel hinge loss-based training method for the underlying models used in CP methods. This approach aims to provide effective control over the compo- sitionality of prediction sets, aligning more closely with the specific needs of pathologists. We evaluate the effectiveness of this training approach using three application-specific metrics tailored to enhance the integration of CP methods into clinical pathology workflows. Our results show that the Hinge Loss-based training approach outperforms the tradi- tional Cross-Entropy method across all evaluation metrics, effectively managing the compositionality of conformal pre- diction sets.

Dataset Guide

Cervical Cancer dataset:-

Center for Recognition and Inspection of Cells (CRIC) Dataset : Download Link

Breast Cancer dataset:-

Breast Cancer Histopathological Database (BreakHis): Download Link

Extract Cells from the Patches

• To extract a cell crop size of 100 x 100 centered on the nucleus from the patches.

python Cropping.py --dataset='path to the cell centre coordinates csv file' --img_dir='path to the cric img patches directory' --cell_img_dir='path to cell img directory'

Feature_Extractor

• To extract features from the image patches.

python Feature_Extractor.py --img_dir='path to the image directory' --csv_file='path to the csv file' --model='Model to be used for feature extraction' --batch_size='Batch size for the dataloader' --num_workers='num workers for dataloader'

Train-Val-Test Split for image features

• To split the image feature containing csv file into train, val and test.

python feature_diet_Train_val_test_split.py --dataset='path to the img feature file' --split='Train/Test split ratio' --folds='No of folds in K-folds'

Train the model in order to get softmax output for test data points.

• Train the model using cross entropy loss

python feature_main.py --loss='cross_entropy' --feat_dir='path to the feature directory' --num_epochs='Number of total training epochs' --model='Model to be used' --batch_size='Batch size for the dataloader' --num_workers='num workers for dataloader' --dataset='Dataset used :- Breast_cancer/Cervical_cancer '

• Train the model using hinge loss for Risk-Based Controlled Set Sizes i.e :- C(High Grade) > C(Low Grade) > C(Normal)

python feature_main.py --loss='hinge_loss' --metric='expt2' --distance_in_hinge_loss='distance_in_hinge_loss'--feat_dir='path to the feature directory' --num_epochs='Number of total training epochs' --model='Model to be used' --batch_size='Batch size for the dataloader' --num_workers='num workers for dataloader' --dataset='Dataset used :- Breast_cancer/Cervical_cancer '

• Train the model using hinge loss for reducing mix of various risks

python feature_main.py --loss='hinge_loss' --metric='class_Overlap_metric' --distance_in_hinge_loss='distance_in_hinge_loss'--feat_dir='path to the feature directory' --num_epochs='Number of total training epochs' --model='Model to be used' --batch_size='Batch size for the dataloader' --num_workers='num workers for dataloader' --dataset='Dataset used :- Breast_cancer/Cervical_cancer '

• Train the model using hinge loss for avoiding confusing classes

python feature_main.py --loss='hinge_loss' --metric='confusion_set_Overlap_metric' --distance_in_hinge_loss='distance_in_hinge_loss'--feat_dir='path to the feature directory' --num_epochs='Number of total training epochs' --model='Model to be used' --batch_size='Batch size for the dataloader' --num_workers='num workers for dataloader' --dataset='Dataset used :- Breast_cancer/Cervical_cancer '

Evaluate Conformal Method

• Eval CP method for baseline results on cervical cancer dataset

python Cervical_cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='1' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for Risk-Based Controlled Set Sizes on cervical cancer dataset

python Cervical_cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='2' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for reducing mix of various risks on cervical cancer dataset

python Cervical_cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='3' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for avoiding confusing classes on cervical cancer dataset

python Cervical_cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='4' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for baseline results on Breast_cancer dataset

python Breast_Cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='1' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for Risk-Based Controlled Set Sizes on Breast_cancer dataset

python Breast_Cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='2' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for reducing mix of various risks on Breast_cancer dataset

python Breast_Cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='3' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

• Eval CP method for avoiding confusing classes on cervical cancer dataset

python Breast_Cancer_Eval_CP_method --Trials='Number of total trials for eval CP method' --softmax_output_file_path='path to the softmax_output_file' --expt_no='4' --split='Calib/test split ratio' --CP_method='CP method to be used' --alpha='value of alpha for CP coverage' 

Grid search for hyperparameter tuning(k_reg, lambd) for RAPS method on breast cancer dataset

• see

breast-cancer-raps-grid-search.ipynb

Grid search for hyperparameter tuning(k_reg, lambd) for RAPS method on cervical cancer dataset

• see

Cervical_cancer_raps_grid_search.ipynb