Report Generation Guide

⚠️ Research Use Only — Models trained and deployed through this platform are for research and development purposes. Clinical deployment requires regulatory review, validation against clinical standards, and human oversight. AI predictions must not be used as the sole basis for medical decisions.

This guide covers generating structured academic paper sections from completed training and evaluation jobs.

Overview

The Report Generation feature produces formatted sections for research papers and technical reports based on your experiment data. It pulls metrics, hyperparameters, and results from completed jobs and structures them into standard academic sections.

Prerequisites

At least one completed (non-archived) training job
(Optional) Completed evaluation jobs with metrics
(Optional) Fairness analysis results

Generate a Report

Step 1 — Open the Report tab

Navigate to http://localhost:8000 and click the Report tab.

Step 2 — Select jobs

The job list shows only eligible jobs:

Training jobs — completed, non-archived
Evaluation jobs — completed

Select one or more training jobs and evaluation jobs to include.

Step 3 — Configure the report

Field	Description	Example
Title	Study/paper title	`"Spine X-ray Classification with DenseNet121"`
Sections	Which sections to generate	`abstract`, `methods`, `results`, `discussion`
Include Fairness	Include fairness analysis	`true` / `false`

Step 4 — Generate

Click Generate Report. The service compiles data from the selected jobs and produces the requested sections.

API

curl -X POST http://localhost:8000/api/report/generate \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Spine X-ray Classification with DenseNet121",
    "training_job_names": ["spine-densenet-v1", "spine-densenet-v2"],
    "evaluation_job_names": ["eval-spine-v2"],
    "sections": ["abstract", "methods", "results", "discussion"],
    "include_fairness": true
  }'

Available Sections

Abstract

A concise summary of the study including:

Task description
Model architecture and key hyperparameters
Dataset summary
Main results (accuracy, AUC)

Methods

Detailed methodology including:

Model architecture and configuration
Training setup (epochs, batch size, learning rate, instance type)
Data source and preprocessing
Evaluation protocol
Fairness analysis methodology (if include_fairness: true)

Results

Quantitative results including:

Training metrics (final train/val accuracy, loss)
Evaluation metrics (accuracy, macro AUC, per-class F1)
Comparison across multiple runs (if multiple jobs selected)
Fairness findings (if included)

Discussion

Interpretation of results including:

Performance analysis
Comparison between model variants (if multiple jobs)
Limitations
Fairness implications (if included)

List Available Jobs

To see which jobs are eligible for report generation:

curl http://localhost:8000/api/report/jobs

Returns only:

Completed, non-archived training jobs
Completed evaluation jobs

Tips

Select related jobs — compare variants of the same model or different architectures on the same dataset
Include evaluation — reports are richer when evaluation metrics are available
Enable fairness — adds demographic distribution discussion to Methods and any performance disparities to Discussion
Use multiple training jobs — the report can compare hyperparameter choices and their impact on performance